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<meta name="dcterms.date" content="2025-01-08">

<title>Replication: Do Voters Reward Value Over Prevention? Evidence from Disaster Policy Preferences</title>
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            // opening and closing the nav tree, so don't dismiss on click)
            const clickEl = placeholderDescriptor.dismissOnClick
              ? toggleContainer
              : toggleTitle;

            const closeToggle = () => {
              if (tocShowing) {
                toggleContainer.classList.remove("expanded");
                toggleContents.style.height = "0px";
                tocShowing = false;
              }
            };

            // Get rid of any expanded toggle if the user scrolls
            window.document.addEventListener(
              "scroll",
              throttle(() => {
                closeToggle();
              }, 50)
            );

            // Handle positioning of the toggle
            window.addEventListener(
              "resize",
              throttle(() => {
                elRect = undefined;
                positionToggle();
              }, 50)
            );

            window.addEventListener("quarto-hrChanged", () => {
              elRect = undefined;
            });

            // Process the click
            clickEl.onclick = () => {
              if (!tocShowing) {
                toggleContainer.classList.add("expanded");
                toggleContents.style.height = null;
                tocShowing = true;
              } else {
                closeToggle();
              }
            };
          });
        };

        // Converts a sidebar from a menu back to a sidebar
        const convertToSidebar = () => {
          for (const child of el.children) {
            child.style.opacity = 1;
            child.style.overflow = null;
          }

          const placeholderEl = window.document.getElementById(
            placeholderDescriptor.id
          );
          if (placeholderEl) {
            placeholderEl.remove();
          }

          el.classList.remove("rollup");
        };

        if (isReaderMode()) {
          convertToMenu();
          isVisible = false;
        } else {
          // Find the top and bottom o the element that is being managed
          const elTop = el.offsetTop;
          const elBottom =
            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;

          if (!isVisible) {
            // If the element is current not visible reveal if there are
            // no conflicts with overlay regions
            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToSidebar();
              isVisible = true;
            }
          } else {
            // If the element is visible, hide it if it conflicts with overlay regions
            // and insert a placeholder toggle (or if we're in reader mode)
            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToMenu();
              isVisible = false;
            }
          }
        }
      }
    };
  };

  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
  for (const tabEl of tabEls) {
    const id = tabEl.getAttribute("data-bs-target");
    if (id) {
      const columnEl = document.querySelector(
        `${id} .column-margin, .tabset-margin-content`
      );
      if (columnEl)
        tabEl.addEventListener("shown.bs.tab", function (event) {
          const el = event.srcElement;
          if (el) {
            const visibleCls = `${el.id}-margin-content`;
            // walk up until we find a parent tabset
            let panelTabsetEl = el.parentElement;
            while (panelTabsetEl) {
              if (panelTabsetEl.classList.contains("panel-tabset")) {
                break;
              }
              panelTabsetEl = panelTabsetEl.parentElement;
            }

            if (panelTabsetEl) {
              const prevSib = panelTabsetEl.previousElementSibling;
              if (
                prevSib &&
                prevSib.classList.contains("tabset-margin-container")
              ) {
                const childNodes = prevSib.querySelectorAll(
                  ".tabset-margin-content"
                );
                for (const childEl of childNodes) {
                  if (childEl.classList.contains(visibleCls)) {
                    childEl.classList.remove("collapse");
                  } else {
                    childEl.classList.add("collapse");
                  }
                }
              }
            }
          }

          layoutMarginEls();
        });
    }
  }

  // Manage the visibility of the toc and the sidebar
  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
    id: "quarto-toc-toggle",
    titleSelector: "#toc-title",
    dismissOnClick: true,
  });
  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
    id: "quarto-sidebarnav-toggle",
    titleSelector: ".title",
    dismissOnClick: false,
  });
  let tocLeftScrollVisibility;
  if (leftTocEl) {
    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
      id: "quarto-lefttoc-toggle",
      titleSelector: "#toc-title",
      dismissOnClick: true,
    });
  }

  // Find the first element that uses formatting in special columns
  const conflictingEls = window.document.body.querySelectorAll(
    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
  );

  // Filter all the possibly conflicting elements into ones
  // the do conflict on the left or ride side
  const arrConflictingEls = Array.from(conflictingEls);
  const leftSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return false;
    }
    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        className.startsWith("column-") &&
        !className.endsWith("right") &&
        !className.endsWith("container") &&
        className !== "column-margin"
      );
    });
  });
  const rightSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return true;
    }

    const hasMarginCaption = Array.from(el.classList).find((className) => {
      return className == "margin-caption";
    });
    if (hasMarginCaption) {
      return true;
    }

    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        !className.endsWith("container") &&
        className.startsWith("column-") &&
        !className.endsWith("left")
      );
    });
  });

  const kOverlapPaddingSize = 10;
  function toRegions(els) {
    return els.map((el) => {
      const boundRect = el.getBoundingClientRect();
      const top =
        boundRect.top +
        document.documentElement.scrollTop -
        kOverlapPaddingSize;
      return {
        top,
        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
      };
    });
  }

  let hasObserved = false;
  const visibleItemObserver = (els) => {
    let visibleElements = [...els];
    const intersectionObserver = new IntersectionObserver(
      (entries, _observer) => {
        entries.forEach((entry) => {
          if (entry.isIntersecting) {
            if (visibleElements.indexOf(entry.target) === -1) {
              visibleElements.push(entry.target);
            }
          } else {
            visibleElements = visibleElements.filter((visibleEntry) => {
              return visibleEntry !== entry;
            });
          }
        });

        if (!hasObserved) {
          hideOverlappedSidebars();
        }
        hasObserved = true;
      },
      {}
    );
    els.forEach((el) => {
      intersectionObserver.observe(el);
    });

    return {
      getVisibleEntries: () => {
        return visibleElements;
      },
    };
  };

  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
  const leftElementObserver = visibleItemObserver(leftSideConflictEls);

  const hideOverlappedSidebars = () => {
    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
    if (tocLeftScrollVisibility) {
      tocLeftScrollVisibility(
        toRegions(leftElementObserver.getVisibleEntries())
      );
    }
  };

  window.quartoToggleReader = () => {
    // Applies a slow class (or removes it)
    // to update the transition speed
    const slowTransition = (slow) => {
      const manageTransition = (id, slow) => {
        const el = document.getElementById(id);
        if (el) {
          if (slow) {
            el.classList.add("slow");
          } else {
            el.classList.remove("slow");
          }
        }
      };

      manageTransition("TOC", slow);
      manageTransition("quarto-sidebar", slow);
    };
    const readerMode = !isReaderMode();
    setReaderModeValue(readerMode);

    // If we're entering reader mode, slow the transition
    if (readerMode) {
      slowTransition(readerMode);
    }
    highlightReaderToggle(readerMode);
    hideOverlappedSidebars();

    // If we're exiting reader mode, restore the non-slow transition
    if (!readerMode) {
      slowTransition(!readerMode);
    }
  };

  const highlightReaderToggle = (readerMode) => {
    const els = document.querySelectorAll(".quarto-reader-toggle");
    if (els) {
      els.forEach((el) => {
        if (readerMode) {
          el.classList.add("reader");
        } else {
          el.classList.remove("reader");
        }
      });
    }
  };

  const setReaderModeValue = (val) => {
    if (window.location.protocol !== "file:") {
      window.localStorage.setItem("quarto-reader-mode", val);
    } else {
      localReaderMode = val;
    }
  };

  const isReaderMode = () => {
    if (window.location.protocol !== "file:") {
      return window.localStorage.getItem("quarto-reader-mode") === "true";
    } else {
      return localReaderMode;
    }
  };
  let localReaderMode = null;

  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;

  // Walk the TOC and collapse/expand nodes
  // Nodes are expanded if:
  // - they are top level
  // - they have children that are 'active' links
  // - they are directly below an link that is 'active'
  const walk = (el, depth) => {
    // Tick depth when we enter a UL
    if (el.tagName === "UL") {
      depth = depth + 1;
    }

    // It this is active link
    let isActiveNode = false;
    if (el.tagName === "A" && el.classList.contains("active")) {
      isActiveNode = true;
    }

    // See if there is an active child to this element
    let hasActiveChild = false;
    for (child of el.children) {
      hasActiveChild = walk(child, depth) || hasActiveChild;
    }

    // Process the collapse state if this is an UL
    if (el.tagName === "UL") {
      if (tocOpenDepth === -1 && depth > 1) {
        el.classList.add("collapse");
      } else if (
        depth <= tocOpenDepth ||
        hasActiveChild ||
        prevSiblingIsActiveLink(el)
      ) {
        el.classList.remove("collapse");
      } else {
        el.classList.add("collapse");
      }

      // untick depth when we leave a UL
      depth = depth - 1;
    }
    return hasActiveChild || isActiveNode;
  };

  // walk the TOC and expand / collapse any items that should be shown

  if (tocEl) {
    walk(tocEl, 0);
    updateActiveLink();
  }

  // Throttle the scroll event and walk peridiocally
  window.document.addEventListener(
    "scroll",
    throttle(() => {
      if (tocEl) {
        updateActiveLink();
        walk(tocEl, 0);
      }
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 5)
  );
  window.addEventListener(
    "resize",
    throttle(() => {
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 10)
  );
  hideOverlappedSidebars();
  highlightReaderToggle(isReaderMode());
});

// grouped tabsets
window.addEventListener("pageshow", (_event) => {
  function getTabSettings() {
    const data = localStorage.getItem("quarto-persistent-tabsets-data");
    if (!data) {
      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
      return {};
    }
    if (data) {
      return JSON.parse(data);
    }
  }

  function setTabSettings(data) {
    localStorage.setItem(
      "quarto-persistent-tabsets-data",
      JSON.stringify(data)
    );
  }

  function setTabState(groupName, groupValue) {
    const data = getTabSettings();
    data[groupName] = groupValue;
    setTabSettings(data);
  }

  function toggleTab(tab, active) {
    const tabPanelId = tab.getAttribute("aria-controls");
    const tabPanel = document.getElementById(tabPanelId);
    if (active) {
      tab.classList.add("active");
      tabPanel.classList.add("active");
    } else {
      tab.classList.remove("active");
      tabPanel.classList.remove("active");
    }
  }

  function toggleAll(selectedGroup, selectorsToSync) {
    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
      const active = selectedGroup === thisGroup;
      for (const tab of tabs) {
        toggleTab(tab, active);
      }
    }
  }

  function findSelectorsToSyncByLanguage() {
    const result = {};
    const tabs = Array.from(
      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
    );
    for (const item of tabs) {
      const div = item.parentElement.parentElement.parentElement;
      const group = div.getAttribute("data-group");
      if (!result[group]) {
        result[group] = {};
      }
      const selectorsToSync = result[group];
      const value = item.innerHTML;
      if (!selectorsToSync[value]) {
        selectorsToSync[value] = [];
      }
      selectorsToSync[value].push(item);
    }
    return result;
  }

  function setupSelectorSync() {
    const selectorsToSync = findSelectorsToSyncByLanguage();
    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
      Object.entries(tabSetsByValue).forEach(([value, items]) => {
        items.forEach((item) => {
          item.addEventListener("click", (_event) => {
            setTabState(group, value);
            toggleAll(value, selectorsToSync[group]);
          });
        });
      });
    });
    return selectorsToSync;
  }

  const selectorsToSync = setupSelectorSync();
  for (const [group, selectedName] of Object.entries(getTabSettings())) {
    const selectors = selectorsToSync[group];
    // it's possible that stale state gives us empty selections, so we explicitly check here.
    if (selectors) {
      toggleAll(selectedName, selectors);
    }
  }
});

function throttle(func, wait) {
  let waiting = false;
  return function () {
    if (!waiting) {
      func.apply(this, arguments);
      waiting = true;
      setTimeout(function () {
        waiting = false;
      }, wait);
    }
  };
}

function nexttick(func) {
  return setTimeout(func, 0);
}
</script>
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  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
  */
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Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove(Tn),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add(Tn),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),u(t),t.classList.contains(On)&&t.classList.add(Cn);let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&V.find(".dropdown-toggle",e).forEach((t=>t.classList.add(Tn))),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=xn.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this)||xn.getOrCreateInstance(this).show()})),g(xn);const Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
//# sourceMappingURL=bootstrap.bundle.min.js.map</script>
<style type="text/css">@font-face {
font-display: block;
font-family: "bootstrap-icons";
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) format("woff");
}
.bi::before,
[class^="bi-"]::before,
[class*=" bi-"]::before {
display: inline-block;
font-family: bootstrap-icons !important;
font-style: normal;
font-weight: normal !important;
font-variant: normal;
text-transform: none;
line-height: 1;
vertical-align: -.125em;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.bi-123::before { content: "\f67f"; }
.bi-alarm-fill::before { content: "\f101"; }
.bi-alarm::before { content: "\f102"; }
.bi-align-bottom::before { content: "\f103"; }
.bi-align-center::before { content: "\f104"; }
.bi-align-end::before { content: "\f105"; }
.bi-align-middle::before { content: "\f106"; }
.bi-align-start::before { content: "\f107"; }
.bi-align-top::before { content: "\f108"; }
.bi-alt::before { content: "\f109"; }
.bi-app-indicator::before { content: "\f10a"; }
.bi-app::before { content: "\f10b"; }
.bi-archive-fill::before { content: "\f10c"; }
.bi-archive::before { content: "\f10d"; }
.bi-arrow-90deg-down::before { content: "\f10e"; }
.bi-arrow-90deg-left::before { content: "\f10f"; }
.bi-arrow-90deg-right::before { content: "\f110"; }
.bi-arrow-90deg-up::before { content: "\f111"; }
.bi-arrow-bar-down::before { content: "\f112"; }
.bi-arrow-bar-left::before { content: "\f113"; }
.bi-arrow-bar-right::before { content: "\f114"; }
.bi-arrow-bar-up::before { content: "\f115"; }
.bi-arrow-clockwise::before { content: "\f116"; }
.bi-arrow-counterclockwise::before { content: "\f117"; }
.bi-arrow-down-circle-fill::before { content: "\f118"; }
.bi-arrow-down-circle::before { content: "\f119"; }
.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
.bi-arrow-down-left-circle::before { content: "\f11b"; }
.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
.bi-arrow-down-left-square::before { content: "\f11d"; }
.bi-arrow-down-left::before { content: "\f11e"; }
.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
.bi-arrow-down-right-circle::before { content: "\f120"; }
.bi-arrow-down-right-square-fill::before { content: "\f121"; }
.bi-arrow-down-right-square::before { content: "\f122"; }
.bi-arrow-down-right::before { content: "\f123"; }
.bi-arrow-down-short::before { content: "\f124"; }
.bi-arrow-down-square-fill::before { content: "\f125"; }
.bi-arrow-down-square::before { content: "\f126"; }
.bi-arrow-down-up::before { content: "\f127"; }
.bi-arrow-down::before { content: "\f128"; }
.bi-arrow-left-circle-fill::before { content: "\f129"; }
.bi-arrow-left-circle::before { content: "\f12a"; }
.bi-arrow-left-right::before { content: "\f12b"; }
.bi-arrow-left-short::before { content: "\f12c"; }
.bi-arrow-left-square-fill::before { content: "\f12d"; }
.bi-arrow-left-square::before { content: "\f12e"; }
.bi-arrow-left::before { content: "\f12f"; }
.bi-arrow-repeat::before { content: "\f130"; }
.bi-arrow-return-left::before { content: "\f131"; }
.bi-arrow-return-right::before { content: "\f132"; }
.bi-arrow-right-circle-fill::before { content: "\f133"; }
.bi-arrow-right-circle::before { content: "\f134"; }
.bi-arrow-right-short::before { content: "\f135"; }
.bi-arrow-right-square-fill::before { content: "\f136"; }
.bi-arrow-right-square::before { content: "\f137"; }
.bi-arrow-right::before { content: "\f138"; }
.bi-arrow-up-circle-fill::before { content: "\f139"; }
.bi-arrow-up-circle::before { content: "\f13a"; }
.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
.bi-arrow-up-left-circle::before { content: "\f13c"; }
.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
.bi-arrow-up-left-square::before { content: "\f13e"; }
.bi-arrow-up-left::before { content: "\f13f"; }
.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
.bi-arrow-up-right-circle::before { content: "\f141"; }
.bi-arrow-up-right-square-fill::before { content: "\f142"; }
.bi-arrow-up-right-square::before { content: "\f143"; }
.bi-arrow-up-right::before { content: "\f144"; }
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.bi-4-square::before { content: "\f7ab"; }
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.bi-5-circle-fill-1::before { content: "\f7ad"; }
.bi-5-circle-fill::before { content: "\f7ae"; }
.bi-5-circle::before { content: "\f7af"; }
.bi-5-square-fill::before { content: "\f7b0"; }
.bi-5-square::before { content: "\f7b1"; }
.bi-6-circle-1::before { content: "\f7b2"; }
.bi-6-circle-fill-1::before { content: "\f7b3"; }
.bi-6-circle-fill::before { content: "\f7b4"; }
.bi-6-circle::before { content: "\f7b5"; }
.bi-6-square-fill::before { content: "\f7b6"; }
.bi-6-square::before { content: "\f7b7"; }
.bi-7-circle-1::before { content: "\f7b8"; }
.bi-7-circle-fill-1::before { content: "\f7b9"; }
.bi-7-circle-fill::before { content: "\f7ba"; }
.bi-7-circle::before { content: "\f7bb"; }
.bi-7-square-fill::before { content: "\f7bc"; }
.bi-7-square::before { content: "\f7bd"; }
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.bi-8-circle-fill-1::before { content: "\f7bf"; }
.bi-8-circle-fill::before { content: "\f7c0"; }
.bi-8-circle::before { content: "\f7c1"; }
.bi-8-square-fill::before { content: "\f7c2"; }
.bi-8-square::before { content: "\f7c3"; }
.bi-9-circle-1::before { content: "\f7c4"; }
.bi-9-circle-fill-1::before { content: "\f7c5"; }
.bi-9-circle-fill::before { content: "\f7c6"; }
.bi-9-circle::before { content: "\f7c7"; }
.bi-9-square-fill::before { content: "\f7c8"; }
.bi-9-square::before { content: "\f7c9"; }
.bi-airplane-engines-fill::before { content: "\f7ca"; }
.bi-airplane-engines::before { content: "\f7cb"; }
.bi-airplane-fill::before { content: "\f7cc"; }
.bi-airplane::before { content: "\f7cd"; }
.bi-alexa::before { content: "\f7ce"; }
.bi-alipay::before { content: "\f7cf"; }
.bi-android::before { content: "\f7d0"; }
.bi-android2::before { content: "\f7d1"; }
.bi-box-fill::before { content: "\f7d2"; }
.bi-box-seam-fill::before { content: "\f7d3"; }
.bi-browser-chrome::before { content: "\f7d4"; }
.bi-browser-edge::before { content: "\f7d5"; }
.bi-browser-firefox::before { content: "\f7d6"; }
.bi-browser-safari::before { content: "\f7d7"; }
.bi-c-circle-1::before { content: "\f7d8"; }
.bi-c-circle-fill-1::before { content: "\f7d9"; }
.bi-c-circle-fill::before { content: "\f7da"; }
.bi-c-circle::before { content: "\f7db"; }
.bi-c-square-fill::before { content: "\f7dc"; }
.bi-c-square::before { content: "\f7dd"; }
.bi-capsule-pill::before { content: "\f7de"; }
.bi-capsule::before { content: "\f7df"; }
.bi-car-front-fill::before { content: "\f7e0"; }
.bi-car-front::before { content: "\f7e1"; }
.bi-cassette-fill::before { content: "\f7e2"; }
.bi-cassette::before { content: "\f7e3"; }
.bi-cc-circle-1::before { content: "\f7e4"; }
.bi-cc-circle-fill-1::before { content: "\f7e5"; }
.bi-cc-circle-fill::before { content: "\f7e6"; }
.bi-cc-circle::before { content: "\f7e7"; }
.bi-cc-square-fill::before { content: "\f7e8"; }
.bi-cc-square::before { content: "\f7e9"; }
.bi-cup-hot-fill::before { content: "\f7ea"; }
.bi-cup-hot::before { content: "\f7eb"; }
.bi-currency-rupee::before { content: "\f7ec"; }
.bi-dropbox::before { content: "\f7ed"; }
.bi-escape::before { content: "\f7ee"; }
.bi-fast-forward-btn-fill::before { content: "\f7ef"; }
.bi-fast-forward-btn::before { content: "\f7f0"; }
.bi-fast-forward-circle-fill::before { content: "\f7f1"; }
.bi-fast-forward-circle::before { content: "\f7f2"; }
.bi-fast-forward-fill::before { content: "\f7f3"; }
.bi-fast-forward::before { content: "\f7f4"; }
.bi-filetype-sql::before { content: "\f7f5"; }
.bi-fire::before { content: "\f7f6"; }
.bi-google-play::before { content: "\f7f7"; }
.bi-h-circle-1::before { content: "\f7f8"; }
.bi-h-circle-fill-1::before { content: "\f7f9"; }
.bi-h-circle-fill::before { content: "\f7fa"; }
.bi-h-circle::before { content: "\f7fb"; }
.bi-h-square-fill::before { content: "\f7fc"; }
.bi-h-square::before { content: "\f7fd"; }
.bi-indent::before { content: "\f7fe"; }
.bi-lungs-fill::before { content: "\f7ff"; }
.bi-lungs::before { content: "\f800"; }
.bi-microsoft-teams::before { content: "\f801"; }
.bi-p-circle-1::before { content: "\f802"; }
.bi-p-circle-fill-1::before { content: "\f803"; }
.bi-p-circle-fill::before { content: "\f804"; }
.bi-p-circle::before { content: "\f805"; }
.bi-p-square-fill::before { content: "\f806"; }
.bi-p-square::before { content: "\f807"; }
.bi-pass-fill::before { content: "\f808"; }
.bi-pass::before { content: "\f809"; }
.bi-prescription::before { content: "\f80a"; }
.bi-prescription2::before { content: "\f80b"; }
.bi-r-circle-1::before { content: "\f80c"; }
.bi-r-circle-fill-1::before { content: "\f80d"; }
.bi-r-circle-fill::before { content: "\f80e"; }
.bi-r-circle::before { content: "\f80f"; }
.bi-r-square-fill::before { content: "\f810"; }
.bi-r-square::before { content: "\f811"; }
.bi-repeat-1::before { content: "\f812"; }
.bi-repeat::before { content: "\f813"; }
.bi-rewind-btn-fill::before { content: "\f814"; }
.bi-rewind-btn::before { content: "\f815"; }
.bi-rewind-circle-fill::before { content: "\f816"; }
.bi-rewind-circle::before { content: "\f817"; }
.bi-rewind-fill::before { content: "\f818"; }
.bi-rewind::before { content: "\f819"; }
.bi-train-freight-front-fill::before { content: "\f81a"; }
.bi-train-freight-front::before { content: "\f81b"; }
.bi-train-front-fill::before { content: "\f81c"; }
.bi-train-front::before { content: "\f81d"; }
.bi-train-lightrail-front-fill::before { content: "\f81e"; }
.bi-train-lightrail-front::before { content: "\f81f"; }
.bi-truck-front-fill::before { content: "\f820"; }
.bi-truck-front::before { content: "\f821"; }
.bi-ubuntu::before { content: "\f822"; }
.bi-unindent::before { content: "\f823"; }
.bi-unity::before { content: "\f824"; }
.bi-universal-access-circle::before { content: "\f825"; }
.bi-universal-access::before { content: "\f826"; }
.bi-virus::before { content: "\f827"; }
.bi-virus2::before { content: "\f828"; }
.bi-wechat::before { content: "\f829"; }
.bi-yelp::before { content: "\f82a"; }
.bi-sign-stop-fill::before { content: "\f82b"; }
.bi-sign-stop-lights-fill::before { content: "\f82c"; }
.bi-sign-stop-lights::before { content: "\f82d"; }
.bi-sign-stop::before { content: "\f82e"; }
.bi-sign-turn-left-fill::before { content: "\f82f"; }
.bi-sign-turn-left::before { content: "\f830"; }
.bi-sign-turn-right-fill::before { content: "\f831"; }
.bi-sign-turn-right::before { content: "\f832"; }
.bi-sign-turn-slight-left-fill::before { content: "\f833"; }
.bi-sign-turn-slight-left::before { content: "\f834"; }
.bi-sign-turn-slight-right-fill::before { content: "\f835"; }
.bi-sign-turn-slight-right::before { content: "\f836"; }
.bi-sign-yield-fill::before { content: "\f837"; }
.bi-sign-yield::before { content: "\f838"; }
.bi-ev-station-fill::before { content: "\f839"; }
.bi-ev-station::before { content: "\f83a"; }
.bi-fuel-pump-diesel-fill::before { content: "\f83b"; }
.bi-fuel-pump-diesel::before { content: "\f83c"; }
.bi-fuel-pump-fill::before { content: "\f83d"; }
.bi-fuel-pump::before { content: "\f83e"; }
.bi-0-circle-fill::before { content: "\f83f"; }
.bi-0-circle::before { content: "\f840"; }
.bi-0-square-fill::before { content: "\f841"; }
.bi-0-square::before { content: "\f842"; }
.bi-rocket-fill::before { content: "\f843"; }
.bi-rocket-takeoff-fill::before { content: "\f844"; }
.bi-rocket-takeoff::before { content: "\f845"; }
.bi-rocket::before { content: "\f846"; }
.bi-stripe::before { content: "\f847"; }
.bi-subscript::before { content: "\f848"; }
.bi-superscript::before { content: "\f849"; }
.bi-trello::before { content: "\f84a"; }
.bi-envelope-at-fill::before { content: "\f84b"; }
.bi-envelope-at::before { content: "\f84c"; }
.bi-regex::before { content: "\f84d"; }
.bi-text-wrap::before { content: "\f84e"; }
.bi-sign-dead-end-fill::before { content: "\f84f"; }
.bi-sign-dead-end::before { content: "\f850"; }
.bi-sign-do-not-enter-fill::before { content: "\f851"; }
.bi-sign-do-not-enter::before { content: "\f852"; }
.bi-sign-intersection-fill::before { content: "\f853"; }
.bi-sign-intersection-side-fill::before { content: "\f854"; }
.bi-sign-intersection-side::before { content: "\f855"; }
.bi-sign-intersection-t-fill::before { content: "\f856"; }
.bi-sign-intersection-t::before { content: "\f857"; }
.bi-sign-intersection-y-fill::before { content: "\f858"; }
.bi-sign-intersection-y::before { content: "\f859"; }
.bi-sign-intersection::before { content: "\f85a"; }
.bi-sign-merge-left-fill::before { content: "\f85b"; }
.bi-sign-merge-left::before { content: "\f85c"; }
.bi-sign-merge-right-fill::before { content: "\f85d"; }
.bi-sign-merge-right::before { content: "\f85e"; }
.bi-sign-no-left-turn-fill::before { content: "\f85f"; }
.bi-sign-no-left-turn::before { content: "\f860"; }
.bi-sign-no-parking-fill::before { content: "\f861"; }
.bi-sign-no-parking::before { content: "\f862"; }
.bi-sign-no-right-turn-fill::before { content: "\f863"; }
.bi-sign-no-right-turn::before { content: "\f864"; }
.bi-sign-railroad-fill::before { content: "\f865"; }
.bi-sign-railroad::before { content: "\f866"; }
.bi-building-add::before { content: "\f867"; }
.bi-building-check::before { content: "\f868"; }
.bi-building-dash::before { content: "\f869"; }
.bi-building-down::before { content: "\f86a"; }
.bi-building-exclamation::before { content: "\f86b"; }
.bi-building-fill-add::before { content: "\f86c"; }
.bi-building-fill-check::before { content: "\f86d"; }
.bi-building-fill-dash::before { content: "\f86e"; }
.bi-building-fill-down::before { content: "\f86f"; }
.bi-building-fill-exclamation::before { content: "\f870"; }
.bi-building-fill-gear::before { content: "\f871"; }
.bi-building-fill-lock::before { content: "\f872"; }
.bi-building-fill-slash::before { content: "\f873"; }
.bi-building-fill-up::before { content: "\f874"; }
.bi-building-fill-x::before { content: "\f875"; }
.bi-building-fill::before { content: "\f876"; }
.bi-building-gear::before { content: "\f877"; }
.bi-building-lock::before { content: "\f878"; }
.bi-building-slash::before { content: "\f879"; }
.bi-building-up::before { content: "\f87a"; }
.bi-building-x::before { content: "\f87b"; }
.bi-buildings-fill::before { content: "\f87c"; }
.bi-buildings::before { content: "\f87d"; }
.bi-bus-front-fill::before { content: "\f87e"; }
.bi-bus-front::before { content: "\f87f"; }
.bi-ev-front-fill::before { content: "\f880"; }
.bi-ev-front::before { content: "\f881"; }
.bi-globe-americas::before { content: "\f882"; }
.bi-globe-asia-australia::before { content: "\f883"; }
.bi-globe-central-south-asia::before { content: "\f884"; }
.bi-globe-europe-africa::before { content: "\f885"; }
.bi-house-add-fill::before { content: "\f886"; }
.bi-house-add::before { content: "\f887"; }
.bi-house-check-fill::before { content: "\f888"; }
.bi-house-check::before { content: "\f889"; }
.bi-house-dash-fill::before { content: "\f88a"; }
.bi-house-dash::before { content: "\f88b"; }
.bi-house-down-fill::before { content: "\f88c"; }
.bi-house-down::before { content: "\f88d"; }
.bi-house-exclamation-fill::before { content: "\f88e"; }
.bi-house-exclamation::before { content: "\f88f"; }
.bi-house-gear-fill::before { content: "\f890"; }
.bi-house-gear::before { content: "\f891"; }
.bi-house-lock-fill::before { content: "\f892"; }
.bi-house-lock::before { content: "\f893"; }
.bi-house-slash-fill::before { content: "\f894"; }
.bi-house-slash::before { content: "\f895"; }
.bi-house-up-fill::before { content: "\f896"; }
.bi-house-up::before { content: "\f897"; }
.bi-house-x-fill::before { content: "\f898"; }
.bi-house-x::before { content: "\f899"; }
.bi-person-add::before { content: "\f89a"; }
.bi-person-down::before { content: "\f89b"; }
.bi-person-exclamation::before { content: "\f89c"; }
.bi-person-fill-add::before { content: "\f89d"; }
.bi-person-fill-check::before { content: "\f89e"; }
.bi-person-fill-dash::before { content: "\f89f"; }
.bi-person-fill-down::before { content: "\f8a0"; }
.bi-person-fill-exclamation::before { content: "\f8a1"; }
.bi-person-fill-gear::before { content: "\f8a2"; }
.bi-person-fill-lock::before { content: "\f8a3"; }
.bi-person-fill-slash::before { content: "\f8a4"; }
.bi-person-fill-up::before { content: "\f8a5"; }
.bi-person-fill-x::before { content: "\f8a6"; }
.bi-person-gear::before { content: "\f8a7"; }
.bi-person-lock::before { content: "\f8a8"; }
.bi-person-slash::before { content: "\f8a9"; }
.bi-person-up::before { content: "\f8aa"; }
.bi-scooter::before { content: "\f8ab"; }
.bi-taxi-front-fill::before { content: "\f8ac"; }
.bi-taxi-front::before { content: "\f8ad"; }
.bi-amd::before { content: "\f8ae"; }
.bi-database-add::before { content: "\f8af"; }
.bi-database-check::before { content: "\f8b0"; }
.bi-database-dash::before { content: "\f8b1"; }
.bi-database-down::before { content: "\f8b2"; }
.bi-database-exclamation::before { content: "\f8b3"; }
.bi-database-fill-add::before { content: "\f8b4"; }
.bi-database-fill-check::before { content: "\f8b5"; }
.bi-database-fill-dash::before { content: "\f8b6"; }
.bi-database-fill-down::before { content: "\f8b7"; }
.bi-database-fill-exclamation::before { content: "\f8b8"; }
.bi-database-fill-gear::before { content: "\f8b9"; }
.bi-database-fill-lock::before { content: "\f8ba"; }
.bi-database-fill-slash::before { content: "\f8bb"; }
.bi-database-fill-up::before { content: "\f8bc"; }
.bi-database-fill-x::before { content: "\f8bd"; }
.bi-database-fill::before { content: "\f8be"; }
.bi-database-gear::before { content: "\f8bf"; }
.bi-database-lock::before { content: "\f8c0"; }
.bi-database-slash::before { content: "\f8c1"; }
.bi-database-up::before { content: "\f8c2"; }
.bi-database-x::before { content: "\f8c3"; }
.bi-database::before { content: "\f8c4"; }
.bi-houses-fill::before { content: "\f8c5"; }
.bi-houses::before { content: "\f8c6"; }
.bi-nvidia::before { content: "\f8c7"; }
.bi-person-vcard-fill::before { content: "\f8c8"; }
.bi-person-vcard::before { content: "\f8c9"; }
.bi-sina-weibo::before { content: "\f8ca"; }
.bi-tencent-qq::before { content: "\f8cb"; }
.bi-wikipedia::before { content: "\f8cc"; }
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<div id="quarto-content" class="page-columns page-rows-contents page-layout-article toc-left">
<div id="quarto-sidebar-toc-left" class="sidebar toc-left">
  <nav id="TOC" role="doc-toc" class="toc-active">
    <h2 id="toc-title">Table of contents</h2>
   
  <ul>
  <li><a href="#summary-information-readme" id="toc-summary-information-readme" class="nav-link active" data-scroll-target="#summary-information-readme">Summary information (README)</a></li>
  <li><a href="#housekeeping" id="toc-housekeeping" class="nav-link" data-scroll-target="#housekeeping">Housekeeping</a>
  <ul class="collapse">
  <li><a href="#load-packages" id="toc-load-packages" class="nav-link" data-scroll-target="#load-packages">load packages</a></li>
  <li><a href="#load-function" id="toc-load-function" class="nav-link" data-scroll-target="#load-function">load function</a></li>
  <li><a href="#session-info" id="toc-session-info" class="nav-link" data-scroll-target="#session-info">Session Info</a></li>
  <li><a href="#load-data" id="toc-load-data" class="nav-link" data-scroll-target="#load-data">load data</a></li>
  </ul></li>
  <li><a href="#main-analysis" id="toc-main-analysis" class="nav-link" data-scroll-target="#main-analysis">Main Analysis</a>
  <ul class="collapse">
  <li><a href="#figure-1-main-results" id="toc-figure-1-main-results" class="nav-link" data-scroll-target="#figure-1-main-results">Figure 1: Main Results</a></li>
  <li><a href="#figure-2-marginal-effect-of-prevention-effort-conditional-on-effectiveness." id="toc-figure-2-marginal-effect-of-prevention-effort-conditional-on-effectiveness." class="nav-link" data-scroll-target="#figure-2-marginal-effect-of-prevention-effort-conditional-on-effectiveness.">Figure 2: Marginal Effect of Prevention Effort Conditional on Effectiveness.</a></li>
  </ul></li>
  <li><a href="#online-appendix" id="toc-online-appendix" class="nav-link" data-scroll-target="#online-appendix">Online Appendix</a>
  <ul class="collapse">
  <li><a href="#figure-a1-natural-disasters-across-africa-1970-2020" id="toc-figure-a1-natural-disasters-across-africa-1970-2020" class="nav-link" data-scroll-target="#figure-a1-natural-disasters-across-africa-1970-2020">Figure A1: Natural Disasters across Africa 1970-2020</a></li>
  <li><a href="#figure-a2-number-of-natural-disasters-by-country-1970-and-2020-and-preparedness-by-country-2007-2011." id="toc-figure-a2-number-of-natural-disasters-by-country-1970-and-2020-and-preparedness-by-country-2007-2011." class="nav-link" data-scroll-target="#figure-a2-number-of-natural-disasters-by-country-1970-and-2020-and-preparedness-by-country-2007-2011.">Figure A2: Number of Natural Disasters by Country (1970 and 2020) and Preparedness by Country (2007-2011).</a></li>
  <li><a href="#figure-a3-malawi-disaster-spending-and-perceived-responsibilities" id="toc-figure-a3-malawi-disaster-spending-and-perceived-responsibilities" class="nav-link" data-scroll-target="#figure-a3-malawi-disaster-spending-and-perceived-responsibilities">Figure A3: Malawi Disaster Spending and Perceived Responsibilities</a></li>
  <li><a href="#figure-a5-help-received-after-the-2015-flood-left-and-satisfaction-with-response-right" id="toc-figure-a5-help-received-after-the-2015-flood-left-and-satisfaction-with-response-right" class="nav-link" data-scroll-target="#figure-a5-help-received-after-the-2015-flood-left-and-satisfaction-with-response-right">Figure A5: Help received after the 2015 flood (left) and satisfaction with response (right)</a></li>
  <li><a href="#figure-a6-actors-help-received-left-and-satisfaction-when-received-help-only-from-mp-right" id="toc-figure-a6-actors-help-received-left-and-satisfaction-when-received-help-only-from-mp-right" class="nav-link" data-scroll-target="#figure-a6-actors-help-received-left-and-satisfaction-when-received-help-only-from-mp-right">Figure A6: Actors help received (left) and satisfaction when received help only from MP (right)</a></li>
  <li><a href="#table-a1-summary-statistics-conjoint-experiment" id="toc-table-a1-summary-statistics-conjoint-experiment" class="nav-link" data-scroll-target="#table-a1-summary-statistics-conjoint-experiment">Table A1: Summary Statistics Conjoint Experiment</a></li>
  <li><a href="#table-a2-summary-statistics" id="toc-table-a2-summary-statistics" class="nav-link" data-scroll-target="#table-a2-summary-statistics">Table A2: Summary Statistics</a></li>
  <li><a href="#table-a4-main-results" id="toc-table-a4-main-results" class="nav-link" data-scroll-target="#table-a4-main-results">Table A4: Main Results</a></li>
  <li><a href="#figure-a9-marginal-means" id="toc-figure-a9-marginal-means" class="nav-link" data-scroll-target="#figure-a9-marginal-means">Figure A9: Marginal Means</a></li>
  <li><a href="#table-a5-main-results-linear-hypothesis-1" id="toc-table-a5-main-results-linear-hypothesis-1" class="nav-link" data-scroll-target="#table-a5-main-results-linear-hypothesis-1">Table A5: Main Results, Linear Hypothesis 1</a></li>
  <li><a href="#table-a6-main-results-linear-hypothesis-2" id="toc-table-a6-main-results-linear-hypothesis-2" class="nav-link" data-scroll-target="#table-a6-main-results-linear-hypothesis-2">Table A6: Main Results, Linear Hypothesis 2</a></li>
  <li><a href="#table-a7-main-results-with-interactions" id="toc-table-a7-main-results-with-interactions" class="nav-link" data-scroll-target="#table-a7-main-results-with-interactions">Table A7: Main Results with Interactions</a></li>
  <li><a href="#figure-a10-heterogeneity-of-amce-by-number-of-contest" id="toc-figure-a10-heterogeneity-of-amce-by-number-of-contest" class="nav-link" data-scroll-target="#figure-a10-heterogeneity-of-amce-by-number-of-contest">Figure A10: Heterogeneity of AMCE by Number of Contest</a></li>
  <li><a href="#figure-a11-distance-to-flood-2015-in-2018." id="toc-figure-a11-distance-to-flood-2015-in-2018." class="nav-link" data-scroll-target="#figure-a11-distance-to-flood-2015-in-2018.">Figure A11: Distance to Flood (2015) in 2018.</a></li>
  <li><a href="#figure-2-amces-by-by-distance-to-2015-flood" id="toc-figure-2-amces-by-by-distance-to-2015-flood" class="nav-link" data-scroll-target="#figure-2-amces-by-by-distance-to-2015-flood">Figure 2: AMCE’s by by distance to 2015 flood</a>
  <ul class="collapse">
  <li><a href="#figure-a13-scatterplot-economic-distress-x-and-distance-to-flood-y" id="toc-figure-a13-scatterplot-economic-distress-x-and-distance-to-flood-y" class="nav-link" data-scroll-target="#figure-a13-scatterplot-economic-distress-x-and-distance-to-flood-y">Figure A13: Scatterplot Economic Distress (X) and Distance to Flood (Y)</a></li>
  <li><a href="#figure-a14-flood-exposure" id="toc-figure-a14-flood-exposure" class="nav-link" data-scroll-target="#figure-a14-flood-exposure">Figure A14: Flood exposure</a></li>
  </ul></li>
  <li><a href="#figure-a15-difference-in-amces-and-marginal-means" id="toc-figure-a15-difference-in-amces-and-marginal-means" class="nav-link" data-scroll-target="#figure-a15-difference-in-amces-and-marginal-means">Figure A15: Difference in AMCEs and Marginal Means</a></li>
  <li><a href="#figure-a16-balance-tests" id="toc-figure-a16-balance-tests" class="nav-link" data-scroll-target="#figure-a16-balance-tests">Figure A16: Balance Tests</a></li>
  <li><a href="#table-a8-manipulation-check" id="toc-table-a8-manipulation-check" class="nav-link" data-scroll-target="#table-a8-manipulation-check">Table A8: Manipulation Check</a></li>
  <li><a href="#figure-a17-difference-in-amces-of-attributes-on-candidate-support-by-disaster-prime" id="toc-figure-a17-difference-in-amces-of-attributes-on-candidate-support-by-disaster-prime" class="nav-link" data-scroll-target="#figure-a17-difference-in-amces-of-attributes-on-candidate-support-by-disaster-prime">Figure A17: Difference in AMCEs of Attributes on Candidate Support, by disaster prime</a></li>
  <li><a href="#table-a9-effect-of-economic-losses-full-models-with-controls" id="toc-table-a9-effect-of-economic-losses-full-models-with-controls" class="nav-link" data-scroll-target="#table-a9-effect-of-economic-losses-full-models-with-controls">Table A9: Effect of Economic Losses, Full Models, with Controls</a></li>
  <li><a href="#figure-a18-media-coverage.-2017-2024-source-gdelt" id="toc-figure-a18-media-coverage.-2017-2024-source-gdelt" class="nav-link" data-scroll-target="#figure-a18-media-coverage.-2017-2024-source-gdelt">Figure A18: Media coverage. 2017-2024, Source: GDELT</a></li>
  <li><a href="#figure-a19-malawi-usage-frequency-of-various-media" id="toc-figure-a19-malawi-usage-frequency-of-various-media" class="nav-link" data-scroll-target="#figure-a19-malawi-usage-frequency-of-various-media">Figure A19: Malawi: Usage Frequency of Various Media</a></li>
  </ul></li>
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<div class="quarto-title-block"><div><h1 class="title">Replication: Do Voters Reward Value Over Prevention? Evidence from Disaster Policy Preferences</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
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  <div class="quarto-title-meta-heading">Author</div>
  <div class="quarto-title-meta-heading">Affiliation</div>
  
    <div class="quarto-title-meta-contents">
    <p class="author">Felix Hartmann <a href="https://orcid.org/0000-0002-6134-7126" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
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    <div class="quarto-title-meta-contents">
        <p class="affiliation">
            Copenhagen Business School
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    <div class="quarto-title-meta-heading">Published</div>
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      <p class="date">January 8, 2025</p>
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<section id="summary-information-readme" class="level1">
<h1>Summary information (README)</h1>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r fold-show code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="sc">----------------------------</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>initial deposit date<span class="sc">:</span> <span class="dv">08</span><span class="sc">/</span><span class="dv">01</span><span class="sc">/</span><span class="dv">2025</span></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>Replication code and data needed to reproduce results presented <span class="cf">in</span><span class="sc">-</span>text and <span class="cf">in</span> the Supplemental Information provided <span class="cf">for</span> the study. </span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="co"># code overview</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;0_replication.qmd&quot;</span> R Quarto script produces all the numeric results, tables, and figures <span class="cf">in</span> the main text and <span class="cf">in</span> the supplementary information</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="co"># files:</span></span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input&quot;</span> contains all data needed <span class="cf">for</span> replication</span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;2_output&quot;</span> contains numeric results, tables, and figures </span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="co"># 1_input:</span></span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/afrobarometer_release-dataset_merge-34ctry_r8_en_2023-03-01.sav&quot;</span></span>
<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> Afrobarometer Merged Round <span class="dv">8</span> <span class="fu">data</span> (<span class="dv">34</span> countries) (<span class="dv">2022</span>)</span>
<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> source<span class="sc">:</span> https<span class="sc">:</span><span class="er">//</span>www.afrobarometer.org<span class="sc">/</span>data<span class="sc">/</span>merged<span class="sc">-</span>data<span class="sc">/</span></span>
<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/emdat_public_2020_12_11_query_uid-GZ254X.xlsx&quot;</span></span>
<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> EM<span class="sc">-</span>DAT<span class="sc">:</span> The CRED<span class="sc">/</span>OFDA International Disaster Database</span>
<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> source<span class="sc">:</span> https<span class="sc">:</span><span class="er">//</span>public.emdat.be<span class="sc">/</span> </span>
<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/ND_Gain_id_infr_06_raw0.csv&quot;</span></span>
<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> Notre Dame Global Adaptation Initiative Country <span class="fu">Index</span> (ND<span class="sc">-</span>GAIN)</span>
<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> variable<span class="sc">:</span> <span class="fu">ID_INFR_06</span> (disaster preparedness)</span>
<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> source<span class="sc">:</span> https<span class="sc">:</span><span class="er">//</span>gain.nd.edu<span class="sc">/</span>our<span class="sc">-</span>work<span class="sc">/</span>country<span class="sc">-</span>index<span class="sc">/</span>download<span class="sc">-</span>data<span class="sc">/</span></span>
<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/custom.geo.json&quot;</span></span>
<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> Africa shapefile</span>
<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/conjoint.rds&quot;</span> </span>
<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> contains all data from the conjoint experiment </span>
<span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a>    <span class="sc">+</span> long format </span>
<span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/covariates.rds&quot;</span></span>
<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> contains all covariates  </span>
<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> merge by case id  to conjoint data</span>
<span id="cb1-39"><a href="#cb1-39" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/euspeech.korpus.RData&quot;</span> </span>
<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> EUSpeech <span class="fu">dataset</span> (Schumacher et al., <span class="dv">2016</span>)</span>
<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/policy_agendas_english.RData&quot;</span> </span>
<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> source<span class="sc">:</span> https<span class="sc">:</span><span class="er">//</span>github.com<span class="sc">/</span>cbpuschmann<span class="sc">/</span>quanteda_mzes<span class="sc">/</span>tree<span class="sc">/</span>master </span>
<span id="cb1-43"><a href="#cb1-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-44"><a href="#cb1-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-45"><a href="#cb1-45" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb1-46"><a href="#cb1-46" aria-hidden="true" tabindex="-1"></a><span class="co"># running time </span></span>
<span id="cb1-47"><a href="#cb1-47" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> For <span class="st">&quot;0_replication.qmd&quot;</span>, about <span class="dv">5</span> min <span class="cf">in</span> total</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="housekeeping" class="level1">
<h1>Housekeeping</h1>
<section id="load-packages" class="level2">
<h2 class="anchored" data-anchor-id="load-packages">load packages</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rm</span>(<span class="at">list=</span><span class="fu">ls</span>())  </span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">modelsummary_format_numeric_latex =</span> <span class="st">&quot;mathmode&quot;</span>)</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span>opts_chunk<span class="sc">$</span><span class="fu">set</span>(<span class="at">echo =</span> <span class="cn">TRUE</span>)</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">require</span>(pacman)) <span class="fu">install.packages</span>(<span class="st">&quot;pacman&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Loading required package: pacman</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>  car,   <span class="co"># linear hypothesis check </span></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>  dplyr,</span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>  tidyverse,</span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>  estimatr,</span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>  here,</span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>  reshape,</span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>  quanteda, <span class="co"># speech data</span></span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>  readtext, <span class="co"># speech data</span></span>
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>  lubridate,<span class="co"># speech data</span></span>
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a>  cregg, <span class="co"># subgroup analysis </span></span>
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a>  compare,</span>
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a>  modelsummary,</span>
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a>  texreg,</span>
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a>  gridExtra,</span>
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a>  grid,</span>
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a>  ggplot2,</span>
<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a>  lattice,</span>
<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a>  vtable,   <span class="co"># Summary Stats</span></span>
<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a>  broom,   <span class="co"># Summary Stats</span></span>
<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a>  broom.helpers,</span>
<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a>  lubridate,</span>
<span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a>  fBasics,</span>
<span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a>  readr,</span>
<span id="cb4-25"><a href="#cb4-25" aria-hidden="true" tabindex="-1"></a>  ggpubr,</span>
<span id="cb4-26"><a href="#cb4-26" aria-hidden="true" tabindex="-1"></a>  foreign,</span>
<span id="cb4-27"><a href="#cb4-27" aria-hidden="true" tabindex="-1"></a>  readxl, <span class="co"># read excel files</span></span>
<span id="cb4-28"><a href="#cb4-28" aria-hidden="true" tabindex="-1"></a>  viridisLite, <span class="co"># colour scheme</span></span>
<span id="cb4-29"><a href="#cb4-29" aria-hidden="true" tabindex="-1"></a>  viridis,<span class="co"># colour scheme</span></span>
<span id="cb4-30"><a href="#cb4-30" aria-hidden="true" tabindex="-1"></a>  sf, <span class="co">#  spatial data</span></span>
<span id="cb4-31"><a href="#cb4-31" aria-hidden="true" tabindex="-1"></a>  ggthemes </span>
<span id="cb4-32"><a href="#cb4-32" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="load-function" class="level2">
<h2 class="anchored" data-anchor-id="load-function">load function</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="do">######################################</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="co"># functions</span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="do">######################################</span></span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>tidy_coefs_with_ref <span class="ot">&lt;-</span> <span class="cf">function</span>(mod_obj, <span class="at">sep =</span> <span class="st">&quot;_&quot;</span>){</span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>  tidy_coefs <span class="ot">&lt;-</span> <span class="fu">tidy</span>(mod_obj) <span class="sc">%&gt;%</span> </span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>    <span class="fu">separate</span>(term, <span class="fu">c</span>(<span class="st">&quot;variable&quot;</span>, <span class="st">&quot;level&quot;</span>), sep, <span class="at">remove =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">variable =</span> <span class="fu">paste0</span>(variable, sep))</span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>  xlevels <span class="ot">&lt;-</span> mod_obj<span class="sc">$</span>xlevels  </span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a>  missing_levels <span class="ot">&lt;-</span> xlevels <span class="sc">%&gt;%</span> </span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a>    <span class="fu">enframe</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a>    <span class="fu">unnest</span>(<span class="at">cols =</span> <span class="fu">c</span>(value)) <span class="sc">%&gt;%</span> </span>
<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>    <span class="fu">set_names</span>(<span class="fu">c</span>(<span class="st">&quot;variable&quot;</span>, <span class="st">&quot;level&quot;</span>))</span>
<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a>  missing_levels <span class="sc">%&gt;%</span> </span>
<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a>    <span class="fu">anti_join</span>(tidy_coefs) <span class="sc">%&gt;%</span> </span>
<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a>    <span class="fu">bind_rows</span>(tidy_coefs) </span>
<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="session-info" class="level2">
<h2 class="anchored" data-anchor-id="session-info">Session Info</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sessionInfo</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Copenhagen
tzcode source: internal

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggthemes_4.2.4       sf_1.0-14            viridis_0.6.5       
 [4] viridisLite_0.4.2    readxl_1.4.3         foreign_0.8-84      
 [7] ggpubr_0.6.0         fBasics_4022.94      broom.helpers_1.14.0
[10] broom_1.0.5          vtable_1.4.4         kableExtra_1.4.0    
[13] lattice_0.21-8       gridExtra_2.3        texreg_1.38.6       
[16] modelsummary_1.4.2   compare_0.2-6        cregg_0.4.0         
[19] readtext_0.91        quanteda_3.3.1       reshape_0.8.9       
[22] here_1.0.1           estimatr_1.0.0       lubridate_1.9.3     
[25] forcats_1.0.0        stringr_1.5.1        purrr_1.0.2         
[28] readr_2.1.4          tidyr_1.3.1          tibble_3.2.1        
[31] ggplot2_3.5.1        tidyverse_2.0.0      dplyr_1.1.4         
[34] car_3.1-2            carData_3.0-5        pacman_0.5.1        

loaded via a namespace (and not attached):
 [1] rstudioapi_0.15.0       jsonlite_1.8.8          magrittr_2.0.3         
 [4] TH.data_1.1-2           estimability_1.4.1      ggstance_0.3.6         
 [7] rmarkdown_2.25          ragg_1.2.5              vctrs_0.6.5            
[10] askpass_1.2.0           rstatix_0.7.2           htmltools_0.5.6        
[13] survey_4.2-1            cellranger_1.1.0        Formula_1.2-5          
[16] KernSmooth_2.23-21      htmlwidgets_1.6.2       plyr_1.8.8             
[19] sandwich_3.0-2          emmeans_1.8.8           zoo_1.8-12             
[22] gt_0.10.0               uuid_1.1-1              lifecycle_1.0.4        
[25] pkgconfig_2.0.3         Matrix_1.6-3            R6_2.5.1               
[28] fastmap_1.2.0           digest_0.6.35           colorspace_2.1-0       
[31] spatial_7.3-16          rprojroot_2.0.3         textshaping_0.3.6      
[34] fansi_1.0.6             timechange_0.3.0        httr_1.4.7             
[37] abind_1.4-5             compiler_4.3.1          proxy_0.4-27           
[40] fontquiver_0.2.1        withr_3.0.0             backports_1.4.1        
[43] DBI_1.1.3               ggsignif_0.6.4          MASS_7.3-60            
[46] openssl_2.1.1           classInt_0.4-10         units_0.8-4            
[49] tools_4.3.1             lmtest_0.9-40           stopwords_2.3          
[52] zip_2.3.0               glue_1.7.0              generics_0.1.3         
[55] gtable_0.3.5            tzdb_0.4.0              class_7.3-22           
[58] data.table_1.14.8       hms_1.1.3               xml2_1.3.5             
[61] utf8_1.2.4              tables_0.9.17           pillar_1.9.0           
[64] mitools_2.4             splines_4.3.1           survival_3.5-5         
[67] tidyselect_1.2.1        fontLiberation_0.1.0    knitr_1.48             
[70] fontBitstreamVera_0.1.1 svglite_2.1.1           xfun_0.44              
[73] timeDate_4022.108       DT_0.29                 stringi_1.8.4          
[76] yaml_2.3.8              evaluate_0.23           codetools_0.2-19       
[79] officer_0.6.7           timeSeries_4031.107     gdtools_0.4.1          
[82] cli_3.6.2               RcppParallel_5.1.7      xtable_1.8-4           
[85] systemfonts_1.1.0       munsell_0.5.1           Rcpp_1.0.12            
[88] coda_0.19-4             mvtnorm_1.2-2           e1071_1.7-13           
[91] scales_1.3.0            insight_0.19.10         flextable_0.9.7        
[94] rlang_1.1.3             fastmatch_1.1-4         multcomp_1.4-25        </code></pre>
</div>
</div>
</section>
<section id="load-data" class="level2">
<h2 class="anchored" data-anchor-id="load-data">load data</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>here<span class="sc">::</span><span class="fu">i_am</span>(<span class="st">&quot;0_replication.qmd&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>here() starts at /Users/felixhartmann/Dropbox (Personal)/Personal Folders/projects/1_active/malawi_floods/Replication/1_public</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>df_long <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;1_input/conjoint.rds&quot;</span>)</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>df_long<span class="ot">&lt;-</span><span class="fu">as_tibble</span>(df_long)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
</section>
<section id="main-analysis" class="level1">
<h1>Main Analysis</h1>
<section id="figure-1-main-results" class="level2">
<h2 class="anchored" data-anchor-id="figure-1-main-results">Figure 1: Main Results</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>df_long_main<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> dplyr<span class="sc">::</span><span class="fu">rename</span>(</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffortPrevention_ =</span> Effort_prevention,</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffortRelief_ =</span> Effort_relief,</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffectivePrevention_ =</span> Effective_prevention,</span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffectiveRelief_ =</span> Effective_relief,</span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>                          <span class="at">Ask_ =</span> Ask,</span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EQ_ =</span> EQ,</span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>                          <span class="at">Honesty_ =</span> Honesty</span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a>                          )</span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a>  res<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>EffortPrevention_<span class="sc">+</span>EffortRelief_<span class="sc">+</span>EffectiveRelief_<span class="sc">+</span>EffectivePrevention_<span class="sc">+</span>Ask_<span class="sc">+</span>EQ_<span class="sc">+</span>Honesty_,</span>
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a>                  <span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID, <span class="at">se_type =</span> <span class="st">&quot;stata&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy_coefs_with_ref</span>() <span class="sc">%&gt;%</span></span>
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a>  <span class="co"># remove intercept</span></span>
<span id="cb11-16"><a href="#cb11-16" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="sc">!</span>(variable <span class="sc">==</span> <span class="st">&#39;(Intercept)_&#39;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb11-17"><a href="#cb11-17" aria-hidden="true" tabindex="-1"></a>  <span class="co"># add space for level</span></span>
<span id="cb11-18"><a href="#cb11-18" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">paste</span>(level, <span class="st">&quot;   &quot;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb11-19"><a href="#cb11-19" aria-hidden="true" tabindex="-1"></a>  <span class="co"># fill value</span></span>
<span id="cb11-20"><a href="#cb11-20" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">estimate =</span> tidyr<span class="sc">::</span><span class="fu">replace_na</span>(estimate, <span class="dv">0</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb11-21"><a href="#cb11-21" aria-hidden="true" tabindex="-1"></a>    <span class="co"># rename</span></span>
<span id="cb11-22"><a href="#cb11-22" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(    </span>
<span id="cb11-23"><a href="#cb11-23" aria-hidden="true" tabindex="-1"></a>  <span class="at">variable=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(variable, </span>
<span id="cb11-24"><a href="#cb11-24" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Ask_&quot;</span><span class="ot">=</span><span class="st">&quot;E. Ask_&quot;</span>, </span>
<span id="cb11-25"><a href="#cb11-25" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Honesty_&quot;</span> <span class="ot">=</span> <span class="st">&quot;G. Honesty_&quot;</span>,</span>
<span id="cb11-26"><a href="#cb11-26" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EQ_&quot;</span> <span class="ot">=</span> <span class="st">&quot;F. EQ_&quot;</span>,</span>
<span id="cb11-27"><a href="#cb11-27" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EffectivePrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;C. EffectivePrevention_&quot;</span>,</span>
<span id="cb11-28"><a href="#cb11-28" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EffectiveRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;D. EffectiveRelief_&quot;</span>,</span>
<span id="cb11-29"><a href="#cb11-29" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EffortPrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;A. EffortPrevention_&quot;</span>,</span>
<span id="cb11-30"><a href="#cb11-30" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EffortRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;B. EffortRelief_&quot;</span></span>
<span id="cb11-31"><a href="#cb11-31" aria-hidden="true" tabindex="-1"></a>  ))    <span class="sc">%&gt;%</span> </span>
<span id="cb11-32"><a href="#cb11-32" aria-hidden="true" tabindex="-1"></a>  <span class="co"># add empty raw</span></span>
<span id="cb11-33"><a href="#cb11-33" aria-hidden="true" tabindex="-1"></a>  <span class="fu">as_tibble</span>() <span class="sc">%&gt;%</span></span>
<span id="cb11-34"><a href="#cb11-34" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(variable) <span class="sc">%&gt;%</span> </span>
<span id="cb11-35"><a href="#cb11-35" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_modify</span>(<span class="sc">~</span> <span class="fu">add_row</span>(.x,<span class="at">.before=</span><span class="dv">0</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb11-36"><a href="#cb11-36" aria-hidden="true" tabindex="-1"></a>  <span class="co">#</span></span>
<span id="cb11-37"><a href="#cb11-37" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_reorder</span>(level, estimate,<span class="at">.na_rm =</span> <span class="cn">TRUE</span>))  <span class="sc">%&gt;%</span> </span>
<span id="cb11-38"><a href="#cb11-38" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> dplyr<span class="sc">::</span><span class="fu">coalesce</span>(level,variable))   <span class="sc">%&gt;%</span> </span>
<span id="cb11-39"><a href="#cb11-39" aria-hidden="true" tabindex="-1"></a>    <span class="co"># rename</span></span>
<span id="cb11-40"><a href="#cb11-40" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(    </span>
<span id="cb11-41"><a href="#cb11-41" aria-hidden="true" tabindex="-1"></a>  <span class="at">level=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(level, </span>
<span id="cb11-42"><a href="#cb11-42" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;A. EffortPrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effort Preparedness&quot;</span>,</span>
<span id="cb11-43"><a href="#cb11-43" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;B. EffortRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effort Relief&quot;</span>,</span>
<span id="cb11-44"><a href="#cb11-44" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;C. EffectivePrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effective Preparedness&quot;</span>,</span>
<span id="cb11-45"><a href="#cb11-45" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;D. EffectiveRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effective Relief&quot;</span>,</span>
<span id="cb11-46"><a href="#cb11-46" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;E. Ask_&quot;</span><span class="ot">=</span><span class="st">&quot;Ask Help&quot;</span>, </span>
<span id="cb11-47"><a href="#cb11-47" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;F. EQ_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb11-48"><a href="#cb11-48" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;G. Honesty_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span></span>
<span id="cb11-49"><a href="#cb11-49" aria-hidden="true" tabindex="-1"></a>  )) <span class="sc">%&gt;%</span></span>
<span id="cb11-50"><a href="#cb11-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_column</span>(<span class="at">indicator =</span> <span class="dv">0</span>) <span class="sc">%&gt;%</span></span>
<span id="cb11-51"><a href="#cb11-51" aria-hidden="true" tabindex="-1"></a>  <span class="co"># replace</span></span>
<span id="cb11-52"><a href="#cb11-52" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb11-53"><a href="#cb11-53" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;A. EffortPrevention_&quot;</span>, <span class="st">&quot;Effort&quot;</span>),</span>
<span id="cb11-54"><a href="#cb11-54" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;B. EffortRelief_&quot;</span>, <span class="st">&quot;Effort&quot;</span>),</span>
<span id="cb11-55"><a href="#cb11-55" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;C. EffectivePrevention_&quot;</span>, <span class="st">&quot;Effective&quot;</span>),</span>
<span id="cb11-56"><a href="#cb11-56" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;D. EffectiveRelief_&quot;</span>, <span class="st">&quot;Effective&quot;</span>),</span>
<span id="cb11-57"><a href="#cb11-57" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;E. Ask_&quot;</span>, <span class="st">&quot;Other&quot;</span>),</span>
<span id="cb11-58"><a href="#cb11-58" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;F. EQ_&quot;</span>, <span class="st">&quot;Other&quot;</span>),</span>
<span id="cb11-59"><a href="#cb11-59" aria-hidden="true" tabindex="-1"></a>        <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;G. Honesty_&quot;</span>, <span class="st">&quot;Other&quot;</span>)</span>
<span id="cb11-60"><a href="#cb11-60" aria-hidden="true" tabindex="-1"></a>      )  <span class="sc">%&gt;%</span></span>
<span id="cb11-61"><a href="#cb11-61" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(    </span>
<span id="cb11-62"><a href="#cb11-62" aria-hidden="true" tabindex="-1"></a>  <span class="at">level=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(level, </span>
<span id="cb11-63"><a href="#cb11-63" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Prevention Effective    &quot;</span><span class="ot">=</span><span class="st">&quot;Yes    &quot;</span>, </span>
<span id="cb11-64"><a href="#cb11-64" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Prevention Not Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot;No    &quot;</span>,</span>
<span id="cb11-65"><a href="#cb11-65" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Relief Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot; Yes    &quot;</span>,</span>
<span id="cb11-66"><a href="#cb11-66" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;No Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot; No    &quot;</span>,</span>
<span id="cb11-67"><a href="#cb11-67" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Prevention Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot;High    &quot;</span>,</span>
<span id="cb11-68"><a href="#cb11-68" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;No Prevention Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot;Low    &quot;</span>,</span>
<span id="cb11-69"><a href="#cb11-69" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Relief Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot; High   &quot;</span>,</span>
<span id="cb11-70"><a href="#cb11-70" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;No Relief Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot; Low    &quot;</span>,</span>
<span id="cb11-71"><a href="#cb11-71" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Ask Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot;  Yes    &quot;</span>,</span>
<span id="cb11-72"><a href="#cb11-72" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Not Ask Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot;  No    &quot;</span>,</span>
<span id="cb11-73"><a href="#cb11-73" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;No Visits    &quot;</span> <span class="ot">=</span> <span class="st">&quot;   No    &quot;</span>,</span>
<span id="cb11-74"><a href="#cb11-74" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Visits    &quot;</span> <span class="ot">=</span> <span class="st">&quot;   Yes    &quot;</span></span>
<span id="cb11-75"><a href="#cb11-75" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-76"><a href="#cb11-76" aria-hidden="true" tabindex="-1"></a>  ))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Joining with `by = join_by(variable, level)`</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>res<span class="sc">$</span>level <span class="ot">&lt;-</span> <span class="fu">factor</span>(res<span class="sc">$</span>level, <span class="at">levels =</span> res<span class="sc">$</span>level)</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>res<span class="ot">&lt;-</span> res <span class="sc">%&gt;%</span>  <span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_rev</span>(<span class="fu">as_factor</span>(level))) </span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>bold.ticks <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Ask Help&quot;</span>,<span class="st">&quot;Corruption&quot;</span>,<span class="st">&quot;Visits&quot;</span>,<span class="st">&quot;Effective Preparedness&quot;</span>,<span class="st">&quot;Effective Relief&quot;</span>,<span class="st">&quot;Effort Preparedness&quot;</span>,<span class="st">&quot;Effort Relief&quot;</span>)</span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>bold.labels <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">levels</span>(res<span class="sc">$</span>level) <span class="sc">%in%</span> bold.ticks, <span class="at">yes =</span> <span class="st">&quot;bold&quot;</span>, <span class="at">no =</span> <span class="st">&quot;plain&quot;</span>)</span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a>fig1<span class="ot">&lt;-</span>res <span class="sc">%&gt;%</span>  </span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span>  level,<span class="at">color =</span> indicator)) <span class="sc">+</span></span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> level, </span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> conf.low, </span>
<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> conf.high),</span>
<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="dv">1</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_colour_colorblind</span>()<span class="sc">+</span></span>
<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_fill_viridis_b(name = &#39;indicator&#39;) + </span></span>
<span id="cb13-17"><a href="#cb13-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb13-18"><a href="#cb13-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;AMCEs&quot;</span>)<span class="sc">+</span></span>
<span id="cb13-19"><a href="#cb13-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Effect on Probability of Support for Candidate&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-20"><a href="#cb13-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Disaster Policy Choice&quot;</span>)<span class="sc">+</span></span>
<span id="cb13-21"><a href="#cb13-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb13-22"><a href="#cb13-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb13-23"><a href="#cb13-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">face=</span>bold.labels))<span class="sc">+</span></span>
<span id="cb13-24"><a href="#cb13-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>,<span class="at">vjust=</span><span class="sc">-</span><span class="fl">2.5</span>))<span class="sc">+</span></span>
<span id="cb13-25"><a href="#cb13-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb13-26"><a href="#cb13-26" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)<span class="sc">+</span></span>
<span id="cb13-27"><a href="#cb13-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;right&quot;</span>)</span>
<span id="cb13-28"><a href="#cb13-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-29"><a href="#cb13-29" aria-hidden="true" tabindex="-1"></a>fig1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<p><img 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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_1.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="figure-2-marginal-effect-of-prevention-effort-conditional-on-effectiveness." class="level2">
<h2 class="anchored" data-anchor-id="figure-2-marginal-effect-of-prevention-effort-conditional-on-effectiveness.">Figure 2: Marginal Effect of Prevention Effort Conditional on Effectiveness.</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Q: Does support for prevention efforts gets lower if voters see it as not being effective?</span></span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="co"># create indicator for occurrence of combination across rows</span></span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>df_long<span class="ot">&lt;-</span>  df_long <span class="sc">%&gt;%</span></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">Effort_prevention_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>      Effort_prevention <span class="sc">==</span> <span class="st">&quot;No Prevention Effort&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>      Effort_prevention <span class="sc">==</span> <span class="st">&quot;Prevention Effort&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">Effort_relief_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>      Effort_relief <span class="sc">==</span> <span class="st">&quot;No Relief Effort&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a>      Effort_relief <span class="sc">==</span> <span class="st">&quot;Relief Effort&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a>      ),</span>
<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a>    <span class="at">Effective_prevention_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a>      Effective_prevention <span class="sc">==</span> <span class="st">&quot;Prevention Not Effective&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb15-16"><a href="#cb15-16" aria-hidden="true" tabindex="-1"></a>      Effective_prevention <span class="sc">==</span> <span class="st">&quot;Prevention Effective&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb15-17"><a href="#cb15-17" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb15-18"><a href="#cb15-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">Effective_relief_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb15-19"><a href="#cb15-19" aria-hidden="true" tabindex="-1"></a>      Effective_relief <span class="sc">==</span> <span class="st">&quot;No Relief&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb15-20"><a href="#cb15-20" aria-hidden="true" tabindex="-1"></a>      Effective_relief <span class="sc">==</span> <span class="st">&quot;Relief Effective&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb15-21"><a href="#cb15-21" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb15-22"><a href="#cb15-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">Ask_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb15-23"><a href="#cb15-23" aria-hidden="true" tabindex="-1"></a>      Ask <span class="sc">==</span> <span class="st">&quot;Not Ask Relief&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb15-24"><a href="#cb15-24" aria-hidden="true" tabindex="-1"></a>      Ask <span class="sc">==</span> <span class="st">&quot;Ask Relief&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb15-25"><a href="#cb15-25" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb15-26"><a href="#cb15-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">EQ_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb15-27"><a href="#cb15-27" aria-hidden="true" tabindex="-1"></a>      EQ <span class="sc">==</span> <span class="st">&quot;No Visits&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb15-28"><a href="#cb15-28" aria-hidden="true" tabindex="-1"></a>      EQ <span class="sc">==</span> <span class="st">&quot;Visits&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb15-29"><a href="#cb15-29" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb15-30"><a href="#cb15-30" aria-hidden="true" tabindex="-1"></a>  <span class="at">Corruption_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb15-31"><a href="#cb15-31" aria-hidden="true" tabindex="-1"></a>    Honesty <span class="sc">==</span> <span class="st">&quot;No Corruption&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb15-32"><a href="#cb15-32" aria-hidden="true" tabindex="-1"></a>    Honesty <span class="sc">==</span> <span class="st">&quot;Corruption&quot;</span>  <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb15-33"><a href="#cb15-33" aria-hidden="true" tabindex="-1"></a>    Honesty <span class="sc">==</span> <span class="st">&quot;Vote Buying&quot;</span>  <span class="sc">~</span> <span class="dv">2</span></span>
<span id="cb15-34"><a href="#cb15-34" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb15-35"><a href="#cb15-35" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb15-36"><a href="#cb15-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-37"><a href="#cb15-37" aria-hidden="true" tabindex="-1"></a>  <span class="co"># count prevention effort + not effective by respondent</span></span>
<span id="cb15-38"><a href="#cb15-38" aria-hidden="true" tabindex="-1"></a>  df_long<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> </span>
<span id="cb15-39"><a href="#cb15-39" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">prevention_ineffective =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(Effort_prevention_num <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> Effective_prevention_num <span class="sc">==</span> <span class="dv">0</span> <span class="sc">~</span> <span class="dv">1</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb15-40"><a href="#cb15-40" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">prevention_ineffective =</span> <span class="fu">replace_na</span>(prevention_ineffective, <span class="dv">0</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb15-41"><a href="#cb15-41" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(CaseID) <span class="sc">%&gt;%</span> </span>
<span id="cb15-42"><a href="#cb15-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Frequency_prevention_ineffective =</span> <span class="fu">sum</span>(prevention_ineffective))</span>
<span id="cb15-43"><a href="#cb15-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-44"><a href="#cb15-44" aria-hidden="true" tabindex="-1"></a>  df_long<span class="sc">$</span>Frequency_prevention_ineffective <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long<span class="sc">$</span>Frequency_prevention_ineffective) </span>
<span id="cb15-45"><a href="#cb15-45" aria-hidden="true" tabindex="-1"></a>  df_long<span class="sc">$</span>Effort_prevention_num <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long<span class="sc">$</span>Effort_prevention_num) </span>
<span id="cb15-46"><a href="#cb15-46" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-47"><a href="#cb15-47" aria-hidden="true" tabindex="-1"></a>  prevention_effort_ineffective<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span></span>
<span id="cb15-48"><a href="#cb15-48" aria-hidden="true" tabindex="-1"></a>  <span class="fu">cj</span>(Choice <span class="sc">~</span> Effort_prevention_num ,<span class="at">id =</span> <span class="sc">~</span> CaseID, <span class="at">estimate =</span> <span class="st">&quot;amce&quot;</span>,<span class="at">by =</span> <span class="sc">~</span>Frequency_prevention_ineffective) <span class="sc">%&gt;%</span>  </span>
<span id="cb15-49"><a href="#cb15-49" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(level <span class="sc">==</span> <span class="dv">1</span>)</span>
<span id="cb15-50"><a href="#cb15-50" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-51"><a href="#cb15-51" aria-hidden="true" tabindex="-1"></a>  prev_ineff<span class="ot">&lt;-</span>prevention_effort_ineffective <span class="sc">%&gt;%</span></span>
<span id="cb15-52"><a href="#cb15-52" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> BY)) <span class="sc">+</span></span>
<span id="cb15-53"><a href="#cb15-53" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb15-54"><a href="#cb15-54" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> BY, </span>
<span id="cb15-55"><a href="#cb15-55" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> estimate <span class="sc">-</span> <span class="fl">1.96</span><span class="sc">*</span>std.error, </span>
<span id="cb15-56"><a href="#cb15-56" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> estimate <span class="sc">+</span> <span class="fl">1.96</span><span class="sc">*</span>std.error),</span>
<span id="cb15-57"><a href="#cb15-57" aria-hidden="true" tabindex="-1"></a>                 <span class="at">linewidth=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb15-58"><a href="#cb15-58" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_colour_viridis_d</span>(<span class="at">name =</span> <span class="st">&#39;Group&#39;</span>) <span class="sc">+</span> </span>
<span id="cb15-59"><a href="#cb15-59" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()<span class="sc">+</span></span>
<span id="cb15-60"><a href="#cb15-60" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb15-61"><a href="#cb15-61" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;AMCEs: Prevention Effort&quot;</span>)<span class="sc">+</span></span>
<span id="cb15-62"><a href="#cb15-62" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.5</span>, <span class="dv">1</span>))<span class="sc">+</span></span>
<span id="cb15-63"><a href="#cb15-63" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Estimate&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;Number of Profiles: High Effort + No Outcome&quot;</span>)<span class="sc">+</span></span>
<span id="cb15-64"><a href="#cb15-64" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb15-65"><a href="#cb15-65" aria-hidden="true" tabindex="-1"></a>  <span class="co">#theme(axis.text.x = element_text(size = 12)) +</span></span>
<span id="cb15-66"><a href="#cb15-66" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb15-67"><a href="#cb15-67" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)</span>
<span id="cb15-68"><a href="#cb15-68" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-69"><a href="#cb15-69" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-70"><a href="#cb15-70" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Prevention effort + effective</span></span>
<span id="cb15-71"><a href="#cb15-71" aria-hidden="true" tabindex="-1"></a>  df_long<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> </span>
<span id="cb15-72"><a href="#cb15-72" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">prevention_effective =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(Effort_prevention_num <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> Effective_prevention_num <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">1</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb15-73"><a href="#cb15-73" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">prevention_effective =</span> <span class="fu">replace_na</span>(prevention_effective, <span class="dv">0</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb15-74"><a href="#cb15-74" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(CaseID) <span class="sc">%&gt;%</span> </span>
<span id="cb15-75"><a href="#cb15-75" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Frequency_prevention_effective =</span> <span class="fu">sum</span>(prevention_effective))</span>
<span id="cb15-76"><a href="#cb15-76" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-77"><a href="#cb15-77" aria-hidden="true" tabindex="-1"></a>  df_long<span class="sc">$</span>Frequency_prevention_effective <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long<span class="sc">$</span>Frequency_prevention_effective) </span>
<span id="cb15-78"><a href="#cb15-78" aria-hidden="true" tabindex="-1"></a>  df_long<span class="sc">$</span>Effort_prevention_num <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long<span class="sc">$</span>Effort_prevention_num) </span>
<span id="cb15-79"><a href="#cb15-79" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-80"><a href="#cb15-80" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb15-81"><a href="#cb15-81" aria-hidden="true" tabindex="-1"></a>  prevention_eff<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> </span>
<span id="cb15-82"><a href="#cb15-82" aria-hidden="true" tabindex="-1"></a>  <span class="co"># could exclude contest 8 (too few cases) and cluster by CaseID, results do not change</span></span>
<span id="cb15-83"><a href="#cb15-83" aria-hidden="true" tabindex="-1"></a>    <span class="fu">cj</span>( Choice <span class="sc">~</span> Effort_prevention_num ,<span class="at">estimate =</span> <span class="st">&quot;amce&quot;</span>,<span class="at">by =</span> <span class="sc">~</span>Frequency_prevention_effective) <span class="sc">%&gt;%</span> </span>
<span id="cb15-84"><a href="#cb15-84" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">filter</span>(level <span class="sc">==</span> <span class="dv">1</span>)</span>
<span id="cb15-85"><a href="#cb15-85" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-86"><a href="#cb15-86" aria-hidden="true" tabindex="-1"></a>  prev_eff<span class="ot">&lt;-</span>prevention_eff <span class="sc">%&gt;%</span></span>
<span id="cb15-87"><a href="#cb15-87" aria-hidden="true" tabindex="-1"></a>  <span class="co">#mutate(level = fct_reorder(estimate, desc(BY))) %&gt;%</span></span>
<span id="cb15-88"><a href="#cb15-88" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> BY)) <span class="sc">+</span></span>
<span id="cb15-89"><a href="#cb15-89" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb15-90"><a href="#cb15-90" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> BY, </span>
<span id="cb15-91"><a href="#cb15-91" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> estimate <span class="sc">-</span> <span class="fl">1.96</span><span class="sc">*</span>std.error, </span>
<span id="cb15-92"><a href="#cb15-92" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> estimate <span class="sc">+</span> <span class="fl">1.96</span><span class="sc">*</span>std.error),</span>
<span id="cb15-93"><a href="#cb15-93" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb15-94"><a href="#cb15-94" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_colour_viridis_d</span>(<span class="at">name =</span> <span class="st">&#39;Group&#39;</span>) <span class="sc">+</span> </span>
<span id="cb15-95"><a href="#cb15-95" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()<span class="sc">+</span></span>
<span id="cb15-96"><a href="#cb15-96" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb15-97"><a href="#cb15-97" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;AMCEs: Prevention Effort&quot;</span>)<span class="sc">+</span></span>
<span id="cb15-98"><a href="#cb15-98" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.5</span>, <span class="dv">1</span>))<span class="sc">+</span></span>
<span id="cb15-99"><a href="#cb15-99" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb15-100"><a href="#cb15-100" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Estimate&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;Number of Profiles: High Effort + Effective Outcome&quot;</span>)<span class="sc">+</span></span>
<span id="cb15-101"><a href="#cb15-101" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb15-102"><a href="#cb15-102" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)</span>
<span id="cb15-103"><a href="#cb15-103" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-104"><a href="#cb15-104" aria-hidden="true" tabindex="-1"></a>fig_updating  <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(prev_ineff,prev_eff,</span>
<span id="cb15-105"><a href="#cb15-105" aria-hidden="true" tabindex="-1"></a>          <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>, <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb15-106"><a href="#cb15-106" aria-hidden="true" tabindex="-1"></a>fig_updating</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<p><img 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g/yGXvOc56Tfve735XXQZy+2/VSKZpVAgQIzEtA//MhPv3PeV1GGx2s/zl4n7jAG1bfTP+zEK33Vf+zXl+5T6nAhrvhrS10gWxUZjlPYPaAoFYW7MlJsgentHbYYYdyX3Zb80BU1TlAs//U58c87nGPK/PJAnR98/nQhz5Ups9uzW8dccQR5ftOc4C+973vLfdvvvnmreyBPH3P0SlBFqBtRfmi7jOXUcwjmAUJW1ngq6xL9vWz0XqYZqNbZxavfJ8Fo8pjXvCCF5Tb57IylzmYqufJOvVlWR75yEdWd+Xr//7f//tyfxYIbWUPjinfF3XPgketLAjcduzf/d3ftVasWLFR2uKYpzzlKa0seNx2TPEmC2yXx2VB8lb2kJpiV9fX6jHxGYm5Y2cu8ylTkVf2sLGybFlAqpVNCt76gz/4g3JbUb/iNT4L2W0txeHlazaipusxxbHZVA95+pe//OVl2hNPPLHMY+ZK9oCr1n/5L/+lbb7cIq/iNZuqopV1ymYe2vZ+WHVsy7TPm7o+uy996UtLu8Jg5utOO+3UN82LXvSijWowLO9BPmPVuZpnlr94X1wvGxXUBgIECMxRQP9zA5z+5waLWNP/fMhD/7PVGlZ/SP9z4/9T6n+2f+94N90CboHP/ldneUigevtR9vCacnRhjA479NBDS6YYHTnXJUZxFkuMAu23FCNPY/7QeGJev+XUU08tk+y9997lyLly44ArL3vZy1LMNxp1H/USf82L0bcx0q9YYgTgwx72sLbyxF9p42n1p512WpFsYl9jdGGxdBvhVuz/xje+kT7wgQ8Ub8vXLHhejqCLaQmi7ocffnj+dO0iUYwQrbZZjDaN0aMxlcHMJUa7ZcHYfHOM2Pza1742M8lG77MHBJXb4jNSHeU4jDKVmVdWYpRqjIqNEb3dli996UspRoLGKMM6l8suuyyfHzT7w0Ras2ZNeaotttii/L6IjTEyND6v//2///cyTa+VSapjr3KOel9d3t0+Y/1G/Y66/s5HgMDCEND/3NDO+p8bLIaxpv+54f8xvfrEnawnqW9WV39okurYqQ3Gta0ub/3PcbWo87YJTHd8V+0GFYinrmXBnHKEUnYreduh2S3G5b7sAmpltye37e/0ptMI0Hi6dBwfP/Ekul5LjCAr0saIwFj6jQDNbncpj4knkNex1DWKrChrFuwt6xAj+7LOWzkaN7t1tvXd7363VR1JGyM9Oy2TMgL08ssvb8Vo3KIt3/nOd25U3OrotO233z5PmwXVWjEqMQsGt5YtW9b61Kc+VR4XI1qL/LLgcCsLmLayTky+/7777mtlwePWH//xH5dp4tqOEcQzl+qI4X6jZOMvz1Gm4rzZbd9t2Q2rTJFp9a/TMfI1zpndlt76X//rf7XivNmtRa0s6NlWx0iTBRzbynTnnXfmT3TPbnkvyx2W8TksfopRrP1GgMa1FyNqi/pHPh/72Mfy8sRo8Sz42vrWt77VyqYfKNNE2q9+9attZSreDKuORX6DvNb12Y1RuoVn9RqJERvF9iwoXK53S3PttdeW1Ri29yCfsRj5G+Ud5HopC2qFAAEC8xDQ/xwcr67fYUUJ9D/1P4fVN9P/LD5VD73W9dnV/2x39o7AIAIb/iyU/U/VsnAF4mndMb9hLFmQo3xieyESc/vFiK7zzz8/35Td5pvPk1nsH/Q15mSM+RljfserrroqHynW7cnuxejPyDueRt9viTkkq6PSYp7MUSzHHXdcPofkbM8V85NmQaS2w8Ik5lOMJeakrD6VPrZtuumm6dnPfnb6zGc+k2L+yVhibr+f//znG81hGQ+sipGNsWRBxfx11P/E/Icxd2cxF+nM0cSdyhMjKZ/85CenLAiTslty8yRxfNQ9lnio1f/9v/83Xw+/H/zgB/nox3xD9k/M+RTXcPyV8aijjkonnHBCfm3/5//8n9P/+3//r0iWv/7H//gf83lpsy/L/On02e29KeZh7LScfvrpKQuC5rue9rSnpZgns1iGWaYiz+I1RnU+97nPTV//+tfbRjRnAcsUDyiL+VXPOeecPHl8LmMOsWKJEbHxkwWgi035qNW5POX9ox/9aDl/azYlRt4O1XlTYx7Vgw46KE8TDx445ZRT8nO+9a1vzcuYTVVQlmHmynzqODOv2bwf1mc3HrZWLMV1Gu933333/KfYV7wOkqZO716fsfj8DON6KerqlQABAr0E9D976fTeN6zfYXEW/c+Uz+Wt/7nhmptP30z/c4Njp7VhfXb1Pzvp2kagj8AgUVJpplsgRm9lD9QpR21lgbOOFf74xz9epokRTJ3mHKwe2GkEaOw/9thjy3ze/va3Vw9pW48Rb9nlm48oK3b0GgF60UUXlfnGcdktzcVhQ32d+Ve8ONdcfuKvozOX6jxYL3zhC2fubnv/hje8oZXdxt2K1yyw17Zv2G9mzsEUo1DDYeZPFoxsZbfkt+JayYKLrexBPW023dq7OjotRhb+6le/6liFmJM1e8BOmWcWJOqYrtgY6YvrKNooRk3OXLKAcpnfJz7xiZm7y/dZELJMF9d2sdRRpupf4GOE68zRpsW54zXmqq1efzESc+YSI2yKNDHPY6el1wjQ+AtzlKPI42//9m87ZVFuizJss802Zfp3v/vd5b5iZdh1LPLt9VrnZ7c4bxYILuvdbf7Zfmnq8B70Mxb1GOR6KerrlQABAnMV0P+cnVydv8P0P1N+t5H+54Y56PU/B+tjD/IprvOzW5y/X98y0vVLo/9ZaHqdZoE0zZVTt8EEzj777PI/7BEc6RRAiZzi4RfVIMgnP/nJnifoFgCNoEARSHnUox7VMY+4XblIkz0tvkzTKwD64x//uDwmjo0HCdWx1PlLLKYeKOodgcBs1GEdVZh1njMDoEUZZ/OajRbMJzDvdPJqcCaby7JTknxb3EZcnDMbTTrQg4uqAfdXv/rVG+WdzQ9a5hm3eHdarrvuulY2ai9PF7fzV4P/dZSpGhyMwGuvJXt6bln+sCmmAqgeM0hAq1cA9IwzzijPEQ+xGuThYh/5yEfKY+IBTjOXYddxZv6d3tf52S3O169zGen6panDe9DPWJRvkOsl0lkIECAwHwH9z9np1fk7TP8ztfQ/26dg0v9stQbpYw/yKa7zs1ucv1/fMtL1S6P/WWh6nWYBt8BnEYOFvlQnn3/xi1/c9XbubbfdNmWjEtP/+T//JyeL223jITSzXeK20LjtPR6UkgVr8oe7ZAGStmyK29832WSTgW5/j4N33HHHtjzilvhRLNnotvLhPLM5X9xqOnOJhz1lQeHcJQsypT/5kz9Jz3zmM3P3bE7LtM8++8w8ZOLfZ09YT3/5l3+Z4sE5xS3tvQq95557dt0dk3IXS0ylMEh+8XCgYqkeX2zLnuCd3vzmN6csqJnfvn3llVdudNtyTEWQzZWZHxLpo07FUs1zWGUq8o7XuB56Ldko29whm/s0T5Z11noln9O+bEREeVzc/p/9IaR8323lwAMPLHdl/7Eq1zutjKuOw/zsdqrXXLfV7d3rMzbXMjuOAAECsxXQ/5ytWHv6Yf4O0/9Mqdfvxjr6evqf7ddzp3d194f0P9vV6/bu9RlrL4l3BOoTEACtz7YROWe3Yafqk9Njrsgi+NipAtUgY3bLeT7/YgREZrvE0+AjABpLdttNqgZAs784pHiqdSxPfepT06677pqv9/snnpJeXWI+x1Esr3/96wcu4yDlifk9s9FxKdomlu9///v5T3b7eIqnNGcjudLzn//8/LVTEHWQc8w3TbR5dR7DIr94Kno2PUIeRC8C3TFnaTVgWKTt9lqdW3NmmmoHNJ7sWTzFfWa66vvsFvXybfX4YmPMTRnX46c//el8U/agofSe97yn2J2/Vp/+/trXvrZtXzXPYZWpeoJB6hgBySIAWszlW81jvuvVDlG/zmJxruwhZ8VqHly+/vrr00477VRuq66Mq47D/uxW6zSf9bq9e33G5lNuxxIgQGBQAf3PQaW6pxv27zD9zw1zu89Ur6Ovp/85U3nj93X3h/Q/283r9tb/bPf2bjwCAqDjcZ+Ys0awswicRKGyuRvzn0ELGKNA5xIAjQe4vOMd74gpGPIAbDzwo1iyW9dTPDwnlle+8pXF5r6v8UCcGKV666235mmzp4/3PWYSE8TIuXiwz8te9rKUPZG5rYjZ3Cwpm1og/4kJxuNhM9kUAQONyGvLaJ5v4qE/EeisY+nVGal2QO+9997yOhm0HHFtxKTu2ZPV2w6JoGYRAP3Hf/zHtgBoPGTqF7/4RZ4+gnoHHHBA27F1lak4ydZbb12sju21eh0OGgDN5gDNr5HiwVHZ1BddA6CTUMex4XY4cd3evT5jHYpjEwECBIYuoP85dNJ5Z6j/+ciuhnX19fQ/u5LnO+ruD+l/tvvX7a3/2e7t3XgEBEDH4z4xZ63efjSXQsXo0XjKdrcnZ3fLM0Z1xlPMI9j5m+w2+J/85Cf5k78jfTECNUYY/umf/mm3LDpuj9GGxYjWf/7nf05//dd/3TFdv43ZvJDpO9/5Tv7E9Wc961lp0KBPv3wH3f+4xz0uxV/h4uneMUL2rLPOKp8OX+QRt2yHfRjGtAQxOnQalk4jS4t6VUc37r333unxj398sWvg1wi6z1ziqfHZw5XygHP2wKG2aRmqoz/jqfExLUN1qatM1XOMez3+uFAsa9asKVZ7vsaUAdW0vZ4C3zOjBbizbu9en7EFyK3KBAiMQUD/szO6/mdnl1Fs7fW7sa6+nv5n75atuz/U++wLb2/d3r0+YwtPW43HJSAAOi75CTjvL3/5yzzwWBQle2jJRvNoFvuqrxF4e8tb3pJvinkq/+Ef/iHF7dmzXeK24wjexfKVr3wlD4BGByPWY8mecJ522GGHfH3Qf170oheVAdAIqnYa7TdIXtnTwHOb6IhGkC1u9x/H8vSnPz3FTyxXXXVVHgjNHkiUvv3tb5cjd7OHP+WjaePW7WlfYo6qYskeqpSGWef4K/w73/nOPPvIN6ZliOsx5v+MJQKff/7nf56vV/+ps0zV84xzPW5Z+e53v5sXIf5gMcgSo5XXrVtXJp3tZ7k8cAGu8F6Aja7KBBaQgP5n98bW/+xuM849dfb19D+7t6z+UHebOvbwrkNVnpMmIAA6aS0ywvJE4LJYHvOYx8wqiHnKKaekc889Nz/8pJNOSkcfffRGI+OKvLu9xujOI488Mg8yxajNuA3+X/7lX9INN9yQHzKb29+Lcxx88MH53JQx+ix+Is94+M5slrjNJYKnxfLGN76xWB3ra9x+fdhhh+U/8YCnWC+CxREQjZGNM0cnjrXANZy82gH9+c9/PtQzZE+IT3/1V3+VXzcxovb444/P517NngCfnydGF3e6daPOMg21gvPILDpExTJoALSaLuaGjfmFLYMJ8B7MSSoCBJopoP/Zud30Pzu7TMLWOvt6+p/dW1h/qLtNHXt416Eqz0kTWDRpBVKe0QjEg2Gqt/bGL9/ZLDH3ZLHE3H7f+ta3ircDv+68887l6MYIlpx33nn57d6RQQzBjyegz3aJeQdf97rXlYcdd9xx+cjJckOflQiaVp9sH7ftztamzyl67o6gWwSGY9RpMeKu0wFRz5izMgJLsdx8883p4osv7pR0qrZVO6DxZPG49votEayP0Zzh+v73v79r8nhATzxgKpZrr702f0hXEWCObfEX+k5LnWXqdL5xbKt2iCLYPsgDxmIu1WKJh5lVb6sptnvtLMC7s4utBAg0X0D/s3Mb6n92dpmUrXX29fQ/u7ey/lB3mzr28K5DVZ6TJiAAOmktMqLyfP3rX8+DZnG6GDV46KGHzurMcft69SE4f//3fz+r44vEkU+xRLDp9NNPz99GIGqrrbYqds3qNUZ8FpNaxxyEBx100EbzZ3bL8Kijjsrn/iz2R9BrruUo8pjNa0w+HaNhI5gZc3/2WuL27Op8ljMf7NPr2Kbue+ITn5ji1vdYov5vfvObe1YlbsGOoOfPfvaz3PWmm27qmb4a5Ix2OO200/L08cCpl770pR2PrbtMHU86y43Lly8vj4hpK2a7xGco5u2N5e67704f/vCHe2YRgenqCJ/ZzuXbM/MFsHPc3vO9XhZAE6kiAQJzFND/7Ayn/9nZZVK21t3X0//s3NLj7g91LtX0bh23t/7n9F5bk1QzAdBJao0RlqU6+XzMMfmoRz1qVmeP4Gc8yb1YokMbo+Zmu0RQqZgQ+W//9m/TjTfemGcxl9vfi3PHA5mOPfbY4m0+SjAmGY8n1q9evbrcXl2J+aie97znpRNPPLHcHE+3/9jHPla+H8VK1TRGeMZ8n92WeMBTEQCNBwLNfAjS//gf/yO/pTtu6465TKdhiRGvcZ0Ut/rH0+iPOeaYtrkmi3qGTTy0KB5qFEsc89a3vrXY3fH1hS98YflArzhPMR1DBOqrv5SrB9ddpuq55rpefQBRBI7PP//8WWUVdY/R1MUS11ZMfdFpufLKK9PLXvaysk3iuhzlKOpOZWratnF7z/d6aZq38hIgMDoB/c92a/3Pdo9JfVd3X0//s3PLj7s/1LlU07t13N76n9N7bU1UzbIggWWBCWQPJ2llQcd4FHb+86lPfWpOAtkTyss8Iq/3vve9bfmcfPLJ5f7slu62fdU32cOOynSRTxZcbd17773VJOX6EUccUabNglLl9k4rWcCmlQW9yvSRdzZKspWNRmtlD7tpZaPYWn/5l3/ZygLAbR6RLrsFoJXd5tsp29aPfvSjtjyXLl3amutP9jCptnNkt0C1skBsmX/W4Wq95jWvaf3TP/1T6xe/+EXrpz/9aetLX/pS64/+6I/KNIsXL2599atfbcsn3uy7775lmhe84AUb7Z/NhiwQW+YVPtkowNkc3jdtNuK3zD/7z1Hf9NnDiMr0UZ7999+/lT04oPWDH/yglY32bGWjD1tPeMIT2tL8t//23/rmGwne9ra3tR0X+Wfz3fY9dphleslLXlKW4ZOf/GTfc2ejlMv02Sjijukf9rCHlWnic5DNl5tfa/F9EEsWfC/3Z38I6JjHzM/qIYcc0vrMZz7TygKqrTPOOCP/DqiWJa7fs88+u2NeddSx44kqG+v87BanqdY/GwlbbG57HSRNHDBM79l+xvpdL20V8oYAAQIDCOh/6n8OcJm0JdH/1P+MC2KY/SH9z4f+/9+tjzpsb/3Ptq80byZAIEaQWRaYQASCIqgTP9m8fK3bb799zgLZw5PKvLI5PVvZ3E5lXoMGQCMAW5QnXrPb8cs8Zq7MJgAax2ZP825lty+35V89V6f1+MV49dVXzzx1+X5mEKVTHoNuy+YbLfMtVrIn17eyEZ0DlTkb7do1wDTNAdDsNu5WNvJzo8B1N/e3v/3tBW/f1wg0V/OJa3yQZZhlqqNzlo0EbqtXUcf4z0UsgwRA77rrrvyPBsWxvV6zhx61zjrrrK50ddSx68n+bUfdn904zSDBzUHSRF7D9J5tB7Tf9RLlsxAgQGA2AvqfD/W9u/3u1P/c+GqatADoMPt6M2ur/5la3f4AP8z+kP5n/wDoML31P2d+0r0ft4Bb4LNeyEJbqnPzxS0X85k7svowpHha9te+9rVZc2a/iFI2irE8bj63v5eZ/NvKq171qvzW/Lidea+99pq5u3wf58/+uph+/OMfp3gCeDHfYZlghCtZcCR/INQJJ5yw0W3tRTGy0VkpG32XshGh6VnPelaxecG8ZiNuU/YfqfTDH/4wxfQG3R6wE9MaRJqPfOQjA9tkgeOUjSgt01fnZSo3dlips0wdTjfrTR/84AfTkUcemeLhY9Ul5p0ddFm5cmX+8K1vfOMbaZ999imnr6geH1NQxFxmF1xwQXruc59b3WV9lgLj9B7G9TLL6kpOgMCUC+h/btzA+p8bm0zyljr7evqf3Vt+nP2h7qWa3j3j9Nb/nN7ralJqtklEYCelMMpBoG6BeHr1b3/72/wn5huNYFA82XG33XZrC8LWXY5B84+H+ER5r7nmmvwnnv7++7//+ymeGGnZIBBPT73ssstSzKUVZnvssUfafffd07bbbrsh0YjXJrFMBcH111+f4meXXXZJ2UjNYvOsX+MhY7/61a/yh3Ztvvnm6ZGPfGTKprtI8R8Ey/AFxuU9rOtl+CJyJECAQDME9D+b0U6zLeUk9vUmsUyF67D6E+PqDxX1WGiv4/Ie1vWy0NpLfXsLCID29rGXAAECBAgQIECAAAECBAgQIECAAIEGC7gFvsGNp+gECBAgQIAAAQIECBAgQIAAAQIECPQWEADt7WMvAQIECBAgQIAAAQIECBAgQIAAAQINFhAAbXDjKToBAgQIECBAgAABAgQIECBAgAABAr0FBEB7+9hLgAABAgQIECBAgAABAgQIECBAgECDBQRAG9x4ik6AAAECBAgQIECAAAECBAgQIECAQG8BAdDePvYSIECAAAECBAgQIECAAAECBAgQINBgAQHQBjeeohMgQIAAAQIECBAgQIAAAQIECBAg0FtAALS3j70ECBAgQIAAAQIECBAgQIAAAQIECDRYQAC0wY2n6AQIECBAgAABAgQIECBAgAABAgQI9BYQAO3tYy8BAgQIECBAgAABAgQIECBAgAABAg0WEABtcOMpOgECBAgQIECAAAECBAgQIECAAAECvQUEQHv72EuAAAECBAgQIECAAAECBAgQIECAQIMFBEAb3HiKToAAAQIECBAgQIAAAQIECBAgQIBAbwEB0N4+9hIgQIAAAQIECBAgQIAAAQIECBAg0GABAdAGN56iEyBAgAABAgQIECBAgAABAgQIECDQW0AAtLePvQQIECBAgAABAgQIECBAgAABAgQINFhAALTBjafoBAgQIECAAAECBAgQIECAAAECBAj0FhAA7e1jLwECBAgQIECAAAECBAgQIECAAAECDRYQAG1w4yk6AQIECBAgQIAAAQIECBAgQIAAAQK9BQRAe/vYS4AAAQIECBAgQIAAAQIECBAgQIBAgwUWN7jsij5Hge9973vpiCOOmOPRwzus1WqVmW2yySbl+jStVOsY9VLPZrdutT2ntS2jhdSz2ddptfTVtozt03rdLsR6TkJbvu9970svfvGLq5ecdQJdBfQ/u9IMfcdC/E4MxEn4Xhx6Y2YZVttzWusYbupZx9UznjyrbRklmNbrdiHWcxLacj79TwHQ8XwnjPWs69atS/fee296wxvekLbYYouxlCW+LO6555783JtuumlasWLFWMpR90kffPDB3DrOs2TJkrTZZpvVfcqx5L927dp0//335+eOOkZdp3G57777Unx+Ytl8883TokXTOYg+PpvFL/SVK1dOY1Pm9VsI30FxvcZ1G8vSpUvTsmXLprI9q99By5cvT4sXT2f3ZlK+g+K6+vjHP15+H07lRaVSQxfQ/xw6adcM9T+70jRyx6R899eNp/9Zt/Do8tf/HJ31KM40Kd9Bw+h/Tuf/EEZxFUzBOQ499ND0sIc9bCw1Wb9+fbrxxhvzc0ewbLvtthtLOeo+aQQFb7vttvw0EeTdaqut6j7lWPKPDsvdd9+dnzvqOK0B7dtvvz2tWbMmr2dcs9Ma6I3PZnxGY9lpp53y12n7J/5zeNNNN+XVisDgtttuO21VzOsT12tct7FE0H7LLbfM16ftn/j+KQLaq1atShEEncYlfp8Uf2zafvvtxxbojTJEANRCYC4C+p9zUZvdMfqfs/Oa9NT6n5PeQoOXT/9zcKsmpNT/HG0rDaP/OZ3Dl0bbDs5GgAABAgQIECBAgAABAgQIECBAgMCECgiATmjDKBYBAgQIECBAgAABAgQIECBAgAABAvMXEACdv6EcCBAgQIAAAQIECBAgQIAAAQIECBCYUAEB0AltGMUiQIAAAQIECBAgQIAAAQIECBAgQGD+AgKg8zeUAwECBAgQIECAAAECBAgQIECAAAECEyogADqhDaNYBAgQIECAAAECBAgQIECAAAECBAjMX0AAdP6GciBAgAABAgQIECBAgAABAgQIECBAYEIFBEAntGEUiwABAgQIECBAgAABAgQIECBAgACB+QsIgM7fUA4ECBAgQIAAAQIECBAgQIAAAQIECEyogADohDaMYhEgQIAAAQIECBAgQIAAAQIECBAgMH8BAdD5G8qBAAECBAgQIECAAAECBAgQIECAAIEJFRAAndCGUSwCBAgQIECAAAECBAgQIECAAAECBOYvIAA6f0M5ECBAgAABAgQIECBAgAABAgQIECAwoQICoBPaMIpFgAABAgQIECBAgAABAgQIECBAgMD8BQRA528oBwIECBAgQIAAAQIECBAgQIAAAQIEJlRAAHRCG0axCBAgQIAAAQIECBAgQIAAAQIECBCYv4AA6PwN5UCAAAECBAgQIECAAAECBAgQIECAwIQKCIBOaMMoFgECBAgQIECAAAECBAgQIECAAAEC8xcQAJ2/oRwIECBAgAABAgQIECBAgAABAgQIEJhQAQHQCW0YxSJAgAABAgQIECBAgAABAgQIECBAYP4CAqDzN5QDAQIECBAgQIAAAQIECBAgQIAAAQITKiAAOqENo1gECBAgQIAAAQIECBAgQIAAAQIECMxfQAB0/oZyIECAAAECBAgQIECAAAECBAgQIEBgQgUWT2i5JrpYF110UfpP/+k/pZUrV6YzzjhjaGV98MEH0ze/+c30z//8z+m3v/1tuvfee9Pee++d9tlnn/SUpzwl7bXXXkM7l4wIECBAgAABAgSaI6D/2Zy2UlICBAgQIEBg8gQEQGfZJnfffXc69thjU6vVmuWRvZPffPPN6aijjkpXXXVVW8Kf/vSnKX4+97nPpWOOOSb98R//cdt+bwgQIECAAAECBKZbQP9zuttX7QgQIECAAIH6BQRAZ2G8evXqdOSRR+ajM2dxWN+kke/b3/72Mvi55557pqc97Wlp++23TxdccEH6/ve/n9auXZs++MEPpkj7kpe8pG+eEhAgQIAAAQIECDRfQP+z+W2oBgQIECBAgMD4BQRAB2yDX/7yl+nDH/5wuvrqqwc8YvBk//AP/5CuuOKK/IBnP/vZ6T3veU9asmRJ/v6QQw5JF154YXrHO96RBz8//vGPp2c+85lpu+22G/wEUhIgQIAAAQIECDROQP+zcU2mwAQIECBAgMCECngIUp+Gue+++9KJJ56Y3vSmN9US/LzrrrvS1772tbwUO+ywQ1vwsyjavvvum2+P9zFPaJG+2O+VAAECBAgQIEBgegT0P6enLdWEAAECBAgQmAwBAdAe7XDxxRenP//zP0+nnnpqPufnJptskl796lenbbbZpsdRs9t19tlnp+jkxvLiF7+4HPk5M5e4JX6XXXbJN0cAdN26dTOTeE+AAAECBAgQINBwAf3Phjeg4hMgQIAAAQITKSAA2qNZzj333HT99dfnKbbddtt0wgknpDe84Q1p0003zbdFQHS+S9zaVCxPfvKTi9WOr0960pPy7bfeems6//zzO6axkQABAgQIECBAoLkC+p/NbTslJ0CAAAECBCZXwBygfdpmyy23TK94xSvSS1/60rT55pv3ST373fFX/lgimLrHHnv0zCAejlQsV155ZeoXMC3SeiVAgAABAgQIEGiOgP5nc9pKSQkQIECAAIFmCAiA9minAw88MB166KFpxYoVPVLNb9fvfve7PIN44vvixb2bY8cddyxPds0115TrVggQIECAAAECBKZDQP9zOtpRLQgQIECAAIHJEugdcZusso68NP1GZM63QDH35/r16/Nstt56677ZrVq1qkwTD0/qtZx00knpX/7lXzomidv5Y7ntttv6Bl07ZjCEja1Wq8wl5jO95ZZbyvfTtFK0b9RpzZo16YEHHpim6pV1iYdzFcs999yT7r333uLtVL1W596944478pHbU1XBf6tM9bqd1s9m9TsoPpfTWs9qW8bvnLVr107jJZs/ILCo2N13351Wr15dvJ2q1+p30O233z6276D7778/d61eX1MFPebK6H/W1wDV7379z/qcR5Wz/ueopEdznurvlGntl1W/g/Q/R3Nd1XmW6neQ/med0g/lPYz+pwBo/e3U9QzV/6AtW7asa7piRzVNBNN6Lb/5zW/Seeed1zFJPFU+lvjSnYSAXPwimIRydMQa4sb4pV79xT7ErCcqq/hFUP1lMFGFG2JhqoGIIWY7cVkthM+m76CJu+zmVSDfQfPiG+jghfC9MBBEQxPpfz7UcL77G3oBdym27/4uMA3dvBB+z/gOaujF2aXYvoO6wAxx8zC+FzwEaYgNMtusqqPkli5d2vfwapoi+t33IAkIECBAgAABAgQI/JuA/qdLgQABAgQIEFiIAkaAjrHVq3N+DjJirjrirBoM7VSFd7/73entb397p10pni561FFHpZh3tDqvaMfENW2MkZA333xznns4FLfl13S6sWUbgeq4VTqW5cuXp3iowTQuMZokbn2PJeoYdZ3GJdqy+ONDXLPVz/A01Tc+m8Vo5XF9R9TtGd+5xe1VS5YsSdtss03dpxxL/tXvoJjPeuXKlWMpR90nje+fYlTbVlttlTbbbLO6TzmW/OO292Iag3F+BxXfg/EAR0vzBKq/u/Q/H5oWqnmt2LvE1e9+/c/eVk3Yq//ZhFYarIz6n4M5NSWV/udoW2oY/U8B0NG2WdvZqkGi4j80bQlmvKmm6fdE+tjfLU3xH8NFixal+Bn3Ev+BmoRy1OFQrdc017P6n2D1rONKGl+e1Wt4fKUY/pnjtqNicc0WEs19XYjfQfHZHNfnszhv1b25V8/CK7n+50NtPs3f/cVnNGo6zfWsfgep53R9l1Wv4Wmqmf7nNLXmQ9+vRY0WyndQfDbH9fkszlv97i/8B30df/Rr0JJOYbrq0+WLkSu9qlm9ZalbcLPX8fYRIECAAAECBAgsbAH9z4Xd/mpPgAABAgQWqoAA6BhbPh5qVNz6fdNNN/UtyY033limmdbbNcsKWiFAgAABAgQIEBi6gP7n0EllSIAAAQIECDRAQAB0zI2022675SWI0Z0xt1ev5dprry1377333uW6FQIECBAgQIAAAQKDCuh/DiolHQECBAgQIDAtAgKgY27Jxz72sWUJfvGLX5TrnVYuvPDCcnP1uHKjFQIECBAgQIAAAQJ9BKr9SP3PPlh2EyBAgAABAlMhIAA65mZ81rOeVZbgrLPOKtdnrsTt7z//+c/zzY95zGOm9onFM+vtPQECBAgQIECAwHAF9D+H6yk3AgQIECBAYPIFBEBrbqMIXF5yySX5zw033LDR2R796EenCGjGcs4556ROQdD7778/ffjDH04PPvhgnu7QQw/NX/1DgAABAgQIECBAYKaA/udMEe8JECBAgACBhS4gAFrzFfCFL3whvf71r89/Pve5z3U82xFHHFFu/9CHPpQi3XXXXZfWrVuX4rb3o446Kv3sZz/L08QtSwcccECZ3goBAgQIECBAgACBqoD+Z1XDOgECBAgQIEAgpcUQxi/wuMc9Ln3gAx9Ixx57bFqzZk06+eST859NN920HPUZpXz4wx+e/uZv/iYtWiRuPf5WUwICBAgQIECAQHMF9D+b23ZKToAAAQIECMxeQCRt9ma1HHHggQemk046Kb8dvghwFre8L168OP3pn/5pvn/VqlW1nF+mBAgQIECAAAECC0tA/3NhtbfaEiBAgACBhSxgBOgcWv+0004b+Kijjz46xc8gy+67756P/IxRoJdddlmK+Zt23nnntOuuu6YttthikCykIUCAAAECBAgQmEIB/c8pbFRVIkCAAAECBEYmIAA6MurBT7TZZpulffbZJ/8Z/CgpCRAgQIAAAQIECMxNQP9zbm6OIkCAAAECBJoh4Bb4ZrSTUhIgQIAAAQIECBAgQIAAAQIECBAgMAcBAdA5oDmEAAECBAgQIECAAAECBAgQIECAAIFmCAiANqOdlJIAAQIECBAgQIAAAQIECBAgQIAAgTkICIDOAc0hBAgQIECAAAECBAgQIECAAAECBAg0Q0AAtBntpJQECBAgQIAAAQIECBAgQIAAAQIECMxBQAB0DmgOIUCAAAECBAgQIECAAAECBAgQIECgGQICoM1oJ6UkQIAAAQIECBAgQIAAAQIECBAgQGAOAgKgc0BzCAECBAgQIECAAAECBAgQIECAAAECzRAQAG1GOyklAQIECBAgQIAAAQIECBAgQIAAAQJzEBAAnQOaQwgQIECAAAECBAgQIECAAAECBAgQaIaAAGgz2kkpCRAgQIAAAQIECBAgQIAAAQIECBCYg4AA6BzQHEKAAAECBAgQIECAAAECBAgQIECAQDMEBECb0U5KSYAAAQIECBAgQIAAAQIECBAgQIDAHAQEQOeA5hACBAgQIECAAAECBAgQIECAAAECBJohIADajHZSSgIECBAgQIAAAQIECBAgQIAAAQIE5iAgADoHNIcQIECAAAECBAgQIECAAAECBAgQINAMAQHQZrSTUhIgQIAAAQIECBAgQIAAAQIECBAgMAcBAdA5oDmEAAECBAgQIECAAAECBAgQIECAAIFmCAiANqOdlJIAAQIECBAgQIAAAQIECBAgQIAAgTkICIDOAc0hBAgQIECAAAECBAgQIECAAAECBAg0Q0AAtBntpJQECBAgQIAAAQIECBAgQIAAAQIECMxBQAB0DmgOIUCAAAECBAgQIECAAAECBAgQIECgGQICoM1oJ6UkQIAAAQIECBAgQIAAAQIECBAgQGAOAgKgc0BzCAECBAgQIECAAAECBAgQIECAAAECzRBY3IxiKiUBAgQIECBAgAABAgQIECBAgAABAnUIrF+/Pl166aVtWd9xxx3pgQceyLfdcsstafHiDWHE7bffPm233XZt6Sf5zYaST3IplY0AAQIECBAgQIAAAQIECBAgQIAAgVoE1q5dm770pS+15X3fffeldevW5ds233zztGjRhhvJDzzwwPTMZz6zLf0kvxEAneTWUTYCBAgQIECAAAECBAgQIECAAAECYxD49a9/nW6//fb8zE9+8pPTihUrxlCK4ZxSAHQ4jnIhQIAAAQIECBAgQIAAAQIECBAg0EiBuL39Gc94RlvZ77zzzvK29z/8wz9Mq1atKvfvuuuu5XoTVgRAm9BKykiAAAECBAgQIECAAAECBAgQIECgJoEIgD7nOc9py/2KK64ob3uP291j3s+mLhtu3m9qDZSbAAECBAgQIECAAAECBAgQIECAAAECXQQEQLvA2EyAAAECBAgQIECAAAECBAgQIECAQPMFBECb34ZqQIAAAQIECBAgQIAAAQIECBAgQIBAFwEB0C4wNhMgQIAAAQIECBAgQIAAAQIECBAg0HwBAdDmt6EaECBAgAABAgQIECBAgAABAgQIECDQRUAAtAuMzQQIECBAgAABAgQIECBAgAABAgQINF9AALT5bagGBAgQIECAAAECBAgQIECAAAECBAh0ERAA7QJjMwECBAgQIECAAAECBAgQIECAAAECzRcQAG1+G6oBAQIECBAgQIAAAQIECBAgQIAAAQJdBARAu8DYTIAAAQIECBAgQIAAAQIECBAgQIBA8wUEQJvfhmpAgAABAgQIECBAgAABAgQIECBAgEAXAQHQLjA2EyBAgAABAgQIECBAgAABAgQIECDQfAEB0Oa3oRoQIECAAAECBAgQIECAAAECBAgQINBFQAC0C4zNBAgQIECAAAECBAgQIECAAAECBAg0X0AAtPltqAYECBAgQIAAAQIECBAgQIAAAQIECHQREADtAmMzAQIECBAgQIAAAQIECBAgQIAAAQLNFxAAbX4bqgEBAgQIECBAgAABAgQIECBAgAABAl0EBEC7wNhMgAABAgQIECBAgAABAgQIECBAgEDzBQRAm9+GakCAAAECBAgQIECAAAECBAgQIECAQBcBAdAuMDYTIECAAAECBAgQIECAAAECBAgQINB8AQHQ5rehGhAgQIAAAQIECBAgQIAAAQIECBAg0EVAALQLjM0ECBAgQIAAAQIECBAgQIAAAQIECDRfQAC0+W2oBgQIECBAgAABAgQIECBAgAABAgQIdBEQAO0CYzMBAgQIECBAgAABAgQIECBAgAABAs0XEABtfhuqAQECBAgQIECAAAECBAgQIECAAAECXQQEQLvA2EyAAAECBAgQIECAAAECBAgQIECAQPMFBECb34ZqQIAAAQIECBAgQIAAAQIECBAgQIBAFwEB0C4wNhMgQIAAAQIECBAgQIAAAQIECBAg0HwBAdDmt6EaECBAgAABAgQIECBAgAABAgQIECDQRUAAtAuMzQQIECBAgAABAgQIECBAgAABAgQINF9AALT5bagGBAgQIECAAAECBAgQIECAAAECBAh0ERAA7QJjMwECBAgQIECAAAECBAgQIECAAAECzRcQAG1+G6oBAQIECBAgQIAAAQIECBAgQIAAAQJdBARAu8DYTIAAAQIECBAgQIAAAQIECBAgQIBA8wUEQJvfhmpAgAABAgQIECBAgAABAgQIECBAgEAXAQHQLjA2EyBAgAABAgQIECBAgAABAgQIECDQfAEB0Oa3oRoQIECAAAECBAgQIECAAAECBAgQINBFQAC0C4zNBAgQIECAAAECBAgQIECAAAECBAg0X0AAtPltqAYECBAgQIAAAQIECBAgQIAAAQIECHQREADtAmMzAQIECBAgQIAAAQIECBAgQIAAAQLNFxAAbX4bqgEBAgQIECBAgAABAgQIECBAgAABAl0EBEC7wNhMgAABAgQIECBAgAABAgQIECBAgEDzBQRAm9+GakCAAAECBAgQIECAAAECBAgQIECAQBcBAdAuMDYTIECAAAECBAgQIECAAAECBAgQINB8AQHQ5rehGhAgQIAAAQIECBAgQIAAAQIECBAg0EVAALQLjM0ECBAgQIAAAQIECBAgQIAAAQIECDRfQAC0+W2oBgQIECBAgAABAgQIECBAgAABAgQIdBEQAO0CYzMBAgQIECBAgAABAgQIECBAgAABAs0XEABtfhuqAQECBAgQIECAAAECBAgQIECAAAECXQQEQLvA2EyAAAECBAgQIECAAAECBAgQIECAQPMFBECb34ZqQIAAAQIECBAgQIAAAQIECBAgQIBAFwEB0C4wNhMgQIAAAQIECBAgQIAAAQIECBAg0HwBAdDmt6EaECBAgAABAgQIECBAgAABAgQIECDQRUAAtAuMzQQIECBAgAABAgQIECBAgAABAgQINF9AALT5bagGBAgQIECAAAECBAgQIECAAAECBAh0ERAA7QJjMwECBAgQIECAAAECBAgQIECAAAECzRcQAG1+G6oBAQIECBAgQIAAAQIECBAgQIAAAQJdBARAu8DYTIAAAQIECBAgQIAAAQIECBAgQIBA8wUEQJvfhmpAgAABAgQIECBAgAABAgQIECBAgEAXAQHQLjA2EyBAgAABAgQIECBAgAABAgQIECDQfAEB0Oa3oRoQIECAAAECBAgQIECAAAECBAgQINBFQAC0C4zNBAgQIECAAAECBAgQIECAAAECBAg0X0AAtPltqAYECBAgQIAAAQIECBAgQIAAAQIECHQREADtAmMzAQIECBAgQIAAAQIECBAgQIAAAQLNFxAAbX4bqgEBAgQIECBAgAABAgQIECBAgAABAl0EBEC7wNhMgAABAgQIECBAgAABAgQIECBAgEDzBQRAm9+GakCAAAECBAgQIECAAAECBAgQIECAQBcBAdAuMDYTIECAAAECBAgQIECAAAECBAgQINB8AQHQ5rehGhAgQIAAAQIECBAgQIAAAQIECBAg0EVAALQLjM0ECBAgQIAAAQIECBAgQIAAAQIECDRfQAC0+W2oBgQIECBAgAABAgQIECBAgAABAgQIdBEQAO0CYzMBAgQIECBAgAABAgQIECBAgAABAs0XEABtfhuqAQECBAgQIECAAAECBAgQIECAAAECXQQEQLvA2EyAAAECBAgQIECAAAECBAgQIECAQPMFBECb34ZqQIAAAQIECBAgQIAAAQIECBAgQIBAFwEB0C4wNhMgQIAAAQIECBAgQIAAAQIECBAg0HwBAdDmt6EaECBAgAABAgQIECBAgAABAgQIECDQRWBxl+02LwCBVquV4mccy8zzznw/jjLVcc6Z9Zr5vo5zjiPPmfWa+X4cZarjnDPrNfN9Heccd57TWsdqvWK9+n7c5sM8f7Ve6jlM2fHkNSntWS3HeCSctckC4/wumnntznzfZNdq2WfWa+b7atomr8+s18z3Ta5btewz6zXzfTXttKxPax2r9Yr16vtpabuoR7Ve6tn8lp2U9qyWY66qAqBzlWvwcevXr89Lf9NNN01ELR544IF0ww03TERZ6izEvffem+Jn2pc777wzxc+0L7fccsu0VzGv30L4bPoOmq5L2XdQ/e15//335ycp+hP1n9EZpkGguF70P0fbmvpBwMgVAABAAElEQVSfo/Wu+2z6n3ULjy5//c/RWY/iTNPc/1y3bl1JeOutt6YHH3ywfD/KlWH0PwVAR9liE3KuRYsemvlgyy23TFtttdVYShXR+7vuuis/96abbpq22GKLsZSj7pPGl8Xq1avz0yxdujQtX7687lOOJf81a9ak4gsp6hh1ncYl2rL4BRDXbFy707jEZ7P4C9u4viPqdo3/iN999935aab5Oyg618UfXnwH1X1V1Z9/9Tto5cqVqfh9Xv+Z289QfN9vsskm7Tu8I9BDoLhe9T97IA1pl/7nkCAnJJvqd7/+54Q0yhyLof85R7gJPWyh/B+4+n/e+A4a1/8Ph9H/FACd0A/TKIoVgaoVK1aM4lQbnSO+/IsAaHSIx1WOjQo25A3xIS0CoIsXL57aekZ7Fl9IEWSZ5vYsAqCbbbZZWrJkyZCvmMnILgKDRQB0Wtsy/nJZDYBOaz2jY1YEQON6ndZ6RnsW30HLli2b6j82Vb+D4vfKOJaiIywAOg795p9T/7P+NtT/rN94lGeI9qx+9+t/jlJ/uOfS/xyu57hzWyj9z+IPmOEd/wce1/8nhtH/9BCkcX9qnJ8AAQIECBAgQIAAAQIECBAgQIAAgdoEBEBro5UxAQIECBAgQIAAAQIECBAgQIAAAQLjFhAAHXcLOD8BAgQIECBAgAABAgQIECBAgAABArUJCIDWRitjAgQIECBAgAABAgQIECBAgAABAgTGLSAAOu4WcH4CBAgQIECAAAECBAgQIECAAAECBGoTEACtjVbGBAgQIECAAAECBAgQIECAAAECBAiMW0AAdNwt4PwECBAgQIAAAQIECBAgQIAAAQIECNQmIABaG62MCRAgQIAAAQIECBAgQIAAAQIECBAYt4AA6LhbwPkJECBAgAABAgQIECBAgAABAgQIEKhNQAC0NloZEyBAgAABAgQIECBAgAABAgQIECAwbgEB0HG3gPMTIECAAAECBAgQIECAAAECBAgQIFCbgABobbQyJkCAAAECBAgQIECAAAECBAgQIEBg3AICoONuAecnQIAAAQIECBAgQIAAAQIECBAgQKA2AQHQ2mhlTIAAAQIECBAgQIAAAQIECBAgQIDAuAUEQMfdAs5PgAABAgQIECBAgAABAgQIECBAgEBtAgKgtdHKmAABAgQIECBAgAABAgQIECBAgACBcQsIgI67BZyfAAECBAgQIECAAAECBAgQIECAAIHaBARAa6OVMQECBAgQIECAAAECBAgQIECAAAEC4xYQAB13Czg/AQIECBAgQIAAAQIECBAgQIAAAQK1CQiA1kYrYwIECBAgQIAAAQIECBAgQIAAAQIExi0gADruFnB+AgQIECBAgAABAgQIECBAgAABAgRqExAArY1WxgQIECBAgAABAgQIECBAgAABAgQIjFtAAHTcLeD8BAgQIECAAAECBAgQIECAAAECBAjUJiAAWhutjAkQIECAAAECBAgQIECAAAECBAgQGLeAAOi4W8D5CRAgQIAAAQIECBAgQIAAAQIECBCoTUAAtDZaGRMgQIAAAQIECBAgQIAAAQIECBAgMG4BAdBxt4DzEyBAgAABAgQIECBAgAABAgQIECBQm4AAaG20MiZAgAABAgQIECBAgAABAgQIECBAYNwCAqDjbgHnJ0CAAAECBAgQIECAAAECBAgQIECgNgEB0NpoZUyAAAECBAgQIECAAAECBAgQIECAwLgFBEDH3QLOT4AAAQIECBAgQIAAAQIECBAgQIBAbQICoLXRypgAAQIECBAgQIAAAQIECBAgQIAAgXELCICOuwWcnwABAgQIECBAgAABAgQIECBAgACB2gQEQGujlTEBAgQIECBAgAABAgQIECBAgAABAuMWEAAddws4PwECBAgQIECAAAECBAgQIECAAAECtQkIgNZGK2MCBAgQIECAAAECBAgQIECAAAECBMYtIAA67hZwfgIECBAgQIAAAQIECBAgQIAAAQIEahMQAK2NVsYECBAgQIAAAQIECBAgQIAAAQIECIxbQAB03C3g/AQIECBAgAABAgQIECBAgAABAgQI1CYgAFobrYwJECBAgAABAgQIECBAgAABAgQIEBi3gADouFvA+QkQIECAAAECBAgQIECAAAECBAgQqE1AALQ2WhkTIECAAAECBAgQIECAAAECBAgQIDBuAQHQcbeA8xMgQIAAAQIECBAgQIAAAQIECBAgUJuAAGhttDImQIAAAQIECBAgQIAAAQIECBAgQGDcAgKg424B5ydAgAABAgQIECBAgAABAgQIECBAoDYBAdDaaGVMgAABAgQIECBAgAABAgQIECBAgMC4BQRAx90Czk+AAAECBAgQIECAAAECBAgQIECAQG0CAqC10cqYAAECBAgQIECAAAECBAgQIECAAIFxCwiAjrsFnJ8AAQIECBAgQIAAAQIECBAgQIAAgdoEBEBro5UxAQIECBAgQIAAAQIECBAgQIAAAQLjFhAAHXcLOD8BAgQIECBAgAABAgQIECBAgAABArUJCIDWRitjAgQIECBAgAABAgQIECBAgAABAgTGLSAAOu4WcH4CBAgQIECAAAECBAgQIECAAAECBGoTEACtjVbGBAgQIECAAAECBAgQIECAAAECBAiMW0AAdNwt4PwECBAgQIAAAQIECBAgQIAAAQIECNQmIABaG62MCRAgQIAAAQIECBAgQIAAAQIECBAYt4AA6LhbwPkJECBAgAABAgQIECBAgAABAgQIEKhNQAC0NloZEyBAgAABAgQIECBAgAABAgQIECAwbgEB0HG3gPMTIECAAAECBAgQIECAAAECBAgQIFCbgABobbQyJkCAAAECBAgQIECAAAECBAgQIEBg3AICoONuAecnQIAAAQIECBAgQIAAAQIECBAgQKA2AQHQ2mhlTIAAAQIECBAgQIAAAQIECBAgQKC5Avfff39av359cyvwbyVf3PgaqAABAgQIECBAgAABAgQIECBAgAABAvMWuP3229NJJ52UTj/99HT++eendevW5Xl+6UtfSgcffHB67Wtfm5761KfO+zyjzsAI0FGLOx8BAgQIECBAgAABAgQIECBAgACBCRM4+eST0+67756OOeaY9JOf/KQMfkYxr7/++hT7n/a0p6WXvvSl6bbbbpuw0vcujgBobx97CRAgQIAAAQIECBAgQIAAAQIECEy1wBFHHJFe//rXpzvuuKNvPU877bT0B3/wB+nqq6/um3ZSEgiATkpLKAcBAgQIECBAgAABAgQIECBAgACBEQscf/zx6YQTTpjVWa+88sr0ohe9KK1evXpWx40rsQDouOSdlwABAgQIECBAgAABAgQIECBAgMAYBWIU57ve9a45leDCCy9Mxx577JyOHfVBAqCjFnc+AgQIECBAgAABAgQIECBAgAABAhMgEAHMtWvXzrkkxx13XLrzzjvnfPyoDhQAHZW08xAgQIAAAQIECBAgQIAAAQIECBCYEIH169enmM9zPst9992XvvnNb84ni5EcKwA6EmYnIUCAAAECBAgQIECAAAECBAgQIDA5Apdffnm6+eab512gc889d9551J2BAGjdwvInQIAAAQIECBAgQIAAAQIECBAgMGEC119//VBKdN111w0lnzozEQCtU1feBAgQIECAAAECBAgQIECAAAECBCZQYPHixUMp1ZIlS4aST52ZCIDWqStvAgQIECBAgAABAgQIECBAgAABAhMo8IhHPGIopRpWPkMpTJdMBEC7wNhMgAABAgQIECBAgAABAgQIECBAYFoFHvnIR6bdd9993tV79rOfPe886s5AALRuYfkTIECAAAECBAgQIECAAAECBAgQmECBV77ylfMq1fbbb5+e85znzCuPURwsADoKZecgQIAAAQIECBAgQIAAAQIECBAgMGECRx99dNp6663nXKr3v//9admyZXM+flQHCoCOStp5CBAgQIAAAQIECBAgQIAAAQIECEyQwKpVq9LnPve5tMkmm8y6VM9//vPTYYcdNuvjxnGAAOg41J2TAAECBAgQIECAAAECBAgQIECAwAQIvPCFL0yf/vSn02yeCh+3vZ9yyilp0aJmhBabUcoJuBgUgQABAgQIECBAgAABAgQIECBAgMA0Crzuda9LZ599dnrCE57Qs3orVqxIcdv7mWeembbccsueaSdp5+JJKoyyECBAgAABAgQIECBAgAABAgQIECAweoGnP/3p6fzzz8+Dm6effnr69re/nW699da0dOnStN9++6VDDjkkvfzlL0877rjj6As3zzMKgM4T0OEECBAgQIAAAQIECBAgQIAAAQIEpkEgbmmPuT3j51Of+lS69NJL82q9613vSvHE96YuboFvasspNwECBAgQIECAAAECBAgQIECAAAECfQUEQPsSSUCAAAECBAgQIECAAAECBAgQIECAQFMFBECb2nLKTYAAAQIECBAgQIAAAQIECBAgQIBAXwEB0L5EEhAgQIAAAQIECBAgQIAAAQIECBAg0FQBAdCmtpxyEyBAgAABAgQIECBAgAABAgQIECDQV0AAtC+RBAQIECBAgAABAgQIECBAgAABAgQINFVAALSpLafcBAgQIECAAAECBAgQIECAAAECBAj0FRAA7UskAQECBAgQIECAAAECBAgQIECAAAECTRUQAG1qyyk3AQIECBAgQIAAAQIECBAgQIAAAQJ9BQRA+xJJQIAAAQIECBAgQIAAAQIECBAgQIBAUwUEQJvacspNgAABAgQIECBAgAABAgQIECBAgEBfAQHQvkQSECBAgAABAgQIECBAgAABAgQIECDQVAEB0Ka2nHITIECAAAECBAgQIECAAAECBAgQINBXQAC0L5EEBAgQIECAAAECBAgQIECAAAECBAg0VUAAtKktp9wECBAgQIAAAQIECBAgQIAAAQIECPQVEADtSyQBAQIECBAgQIAAAQIECBAgQIAAAQJNFRAAbWrLKTcBAgQIECBAgAABAgQIECBAgAABAn0FBED7EklAgAABAgQIECBAgAABAgQIECBAgEBTBQRAm9pyyk2AAAECBAgQIECAAAECBAgQIECAQF8BAdC+RBIQIECAAAECBAgQIECAAAECBAgQINBUAQHQpracchMgQIAAAQIECBAgQIAAAQIECBAg0FdAALQvkQQECBAgQIAAAQIECBAgQIAAAQIECDRVQAC0qS2n3AQIECBAgAABAgQIECBAgAABAgQI9BUQAO1LJAEBAgQIECBAgAABAgQIECBAgAABAk0VEABtasspNwECBAgQIECAAAECBAgQIECAAAECfQUEQPsSSUCAAAECBAgQIECAAAECBAgQIECAQFMFBECb2nLKTYAAAQIECBAgQIAAAQIECBAgQIBAXwEB0L5EEhAgQIAAAQIECBAgQIAAAQIECBAg0FSBxU0t+CjL/bvf/S594QtfSJdeemmK9R122CE9/vGPT/vss0868MAD0/Lly+ddnB/84Afp3HPPHSifzTbbLL3lLW8ZKK1EBAgQIECAAAECzRPQ/2xemykxAQIECBAgMLkCAqB92uaUU05JJ510UnrwwQfLlFdffXWKnzPOOCN9/etfTx/96EfTypUry/1zWfnWt76Vvvvd7w506Oabby4AOpCURAQIECBAgACB5gnofzavzZSYAAECBAgQmGwBt8D3aJ8IcH7yk5/Mg59Lly5Nz3ve89LRRx+dXvOa16Q99tgjP/Liiy9Ob37zm9Ntt93WI6f+uy677LL+iaQgQIAAAQIECBCYagH9z6luXpUjQIAAAQIExiRgBGgX+Ntvvz0dd9xx+d4YcXnsscem/fbbr0z92te+Nn3wgx9M3/nOd9KVV16ZPvvZz6Yjjzyy3D+blfvuuy9de+21+SFPetKT0rvf/e6ehy9aJG7dE8hOAgQIECBAgEADBfQ/G9hoikyAAAECBAg0QkAAtEszfeUrX0lr167N9x5++OFtwc/YuHjx4vTe9743nxP0kksuSWeeeWY67LDD0ooVK7rk2H3zFVdckVqtVp7gsY99bNp22227J7aHAAECBAgQIEBgKgX0P6eyWVWKAAECBAgQmAABQwm7NELcfhRLPODo+c9/fsdUMRLzFa94Rb4vRnFGEHQuS/X298c85jFzycIxBAgQIECAAAECDRfQ/2x4Ayo+AQIECBAgMLECAqAdmub6668v5/SM296XLFnSIdVDm/bff/9yXzzIaC6LAOhc1BxDgAABAgQIEJgeAf3P6WlLNSFAgAABAgQmT0AAtEObxIONimXPPfcsVju+br311uUt6zEX6FyWIgC6atWqtOOOO+ZZxC3xN9xwQ7rzzjvnkqVjCBAgQIAAAQIEGiSg/9mgxlJUAgQIECBAoHEC5gDt0GTFA4li184779whRfumCFreeuutKW6Dv+WWW9J2223XnqDHuwcffDB/iFIkidvfL7roovyBShdeeGFas2ZNfuQOO+yQYm7QmGP04Q9/eI/c7CJAgAABAgQIEGiigP5nE1tNmQkQIECAAIGmCAiAdmip1atXl1tjhGe/JUZuFstdd901qwDoNddcUz5sKYKeb3rTm4qsytebbropxc+PfvSjFA9keslLXlLu67by+c9/Pv3gBz/ouHvlypX59jvuuCMtXbq0Y5q6NxYPfYrzrFu3rpxyoO7zjjr/9evXl6eMgHYEvKdxiTYslvj8FMH7Ytu0vD7wwANlVWJ0dswDPI1L9bq97bbbprGK5YPnonLRrtNaz+p3Tnwuq5/VaWrYar3uueee/A+S01S/oi4zv4M22WSTYtdIX++///78fNXvipEWYEpPpv9Zf8Pqf9ZvPMozVL/79T9HKV/Puaq/U6a1X1b9DtL/rOc6GmWu1e+gae5/Vv8/EfGuTTfddJTM5bmG0f8UAC05N6xUO6DLli3bsKPLWjXNbAM/xe3vkXWMIN1ss83SE5/4xPyp89tss0266qqr0ve+97103XXX5UGl448/PkUA86CDDupSmoc2/+pXv8qP65Ro3333zTfHBVRcRJ3SjWpb/CKYhHLUXd/4pb4Q6hm/CKq/DOp2HVf+1UDEuMowivMuhGt2oXwHReel2oEZxfUzjnMslO+gtWvXjoM3P+dC+F4YB67+52jVF8p3v/7naK+rus+m/1m38OjyXyjfQfqfo7um6jpT9Y8T0f8cVz9wGOcVAO1wldx7773l1kFGSFbTzLZRqgHQuHX+uOOOS7vttlt5/lh57Wtfmz7+8Y+nr3/96/n2E088MT3pSU9Kg4xObcvIGwIECBAgQIAAgYkU0P+cyGZRKAIECBAgQGBKBARAOzTk4sUbWAYZLVNNUw2Gdsh6o02HHHJIetzjHpfiyZ/Petaz0k477bRRmhgVetRRR6VLLrkkRcA0br398pe/nN74xjdulLbYcPTRR3fdf95556W43X7bbbdN22+/fXHISF/jrwgxb2os4T2twdz4C0nxIKtox2L6gZFij+Bk8Z+2YuRK1DHqOo1LtGUx6iqu2ep3xTTVNz6bxV/6xvUdUbdn9TtoyZIlqTqVSd3nHmX+8Ue5uFUlluXLl6cttthilKcf2bni+6cIHi2U76C4S2TctyCN6xb8kV1YIz5R9XdKtW/ZrRjVNPqf3ZTat1e/+/U/222a+E7/s4mt1r3M+p/dbZq2Z1r7n1dffXU+dVbRHnEHb/zEsvnmm6fqncHxvlNspzi2Sa/V/kn8H3g2z7wZZj2LwYbz6X9uiPQNs2QNzyv+k1gsRbCjeN/ptWiI2BcX+myWRzziESl++i3xn5y/+Iu/SMccc0yetDpytNOxEdyMn07L5Zdfnm+OC7l6MXdKW9e2IrgS+ccFPK5y1FW/It/qf05ivshprWd1LsyFUs/4TE5rexbXb7xOax2rn81p/g6qTkfhs1m9spu5Xu3wjfM7qPj8VMvTTNHJKrX+Z/3tof9Zv/Eoz6D/OUrt0Z5L/3O03sM+27T2P88444y25wZEHKiIF8UAoBhUUSzxgOtXvvKVxdtGv1b7e03vfwqAdrgUq0HMYlRbh2TlpmLUSWyoHlsmGNLKnnvuWeZ05ZVXlutWCBAgQIAAAQIEmi1Q7UPqfza7LZWeAAECBKZfIJ7TEg+1jmWvvfZKO+644/RXuuE1FADt0IA777xzuTWevt5vKdJENHzLLbfsl3zO+6u3oha3Vc85MwcSIECAAAECBAhMjID+58Q0hYIQIECAAIGNBPbbb79yyqXYGfGfePp7LDGt4aMf/eh8Pf7ZYYcdynUrkyMgANqhLR71qEeVW3/3u9+V651WYnj3DTfckO/afffd2+Z96JR+5rb4C//tt9+eP0lrjz32mLm77X0RaI2Nu+yyS9s+bwgQIECAAAECBJoroP/Z3LZTcgIECBCYfoFnPOMZbZWMKYHuuOOOfNtTnvKUtP/++7ft92byBARAO7RJ3Goe8zc88MAD6YILLuiQYsOmf/3Xfy0nwn3sYx+7YccAa3Hr/MEHH5zigxOT13/jG9/oGUD9zW9+U+a66667lutWCBAgQIAAgekSWLNmTTruuOPaKhX9hmJerbhdOkYeFMsBBxyQnva0pxVvvTZQQP+zgY2myAQIECBAgEBjBBY1pqQjLOiKFSvSH/7hH+ZnvOqqq9Kvf/3rrmc/66yzyn1PfepTy/VBVuI8RdA0Js/98Y9/3POwL37xi+X+pz/96eW6FQIECBAgQGD6BKJvUP259tprUzzIMH4iGFrdVzyYaPoUFk6N9D8XTlurKQECBAgQIDB6AQHQLuYveMELyj0f/ehH0913312+L1Z++MMfpngSWCy77bZbimHPM5cbb7wxXXLJJflPcat8NU01kHniiSd2PE+kP/XUU8vRqDG3xEEHHVTNxjoBAgQIECAwRQLxxM2tttqq7Semzbnlllvyn3hieHX/smXLpqj2C7cq+p8Lt+3VnAABAgQIEKhXwC3wXXzjNrIYBRqjMiOA+da3vjW9+c1vTo9//OPTXXfdlb73ve+lT3ziE/nt6/GflMMOOyzF68zlC1/4QvrqV7+ab45O7Tvf+c62JC9/+cvTOeecky666KIUc3y+/vWvT295y1tSTLAbt8XHCNQvf/nL6cwzz8yPi21Rlk7nasvYGwIECBAgQKCxAhHQPOKII9rKH7e9X3rppfm2ww8/PFUfjtiW0JvGCuh/NrbpFJwAAQIECBCYcAEB0B4N9N73vje9//3vTz/96U/z283e9ra35fNtzbzNLAKjs739vTjt4sWL0wc+8IE8qBm3tsVDl971rnflu2NfMddXbIgniX3oQx9Ke+21V3G4VwIECBAgQIAAgSkS0P+cosZUFQIECBAgQGBiBNwC36Mpttxyy/Sxj30svfrVr04rV67MU1aDn/HU94985CMpRnHOZ4kRHJ///OdTjOaI0R3FUgQ/V61alZ797Genk08+WfCzwPFKgAABAgQIEJhCAf3PKWxUVSJAgAABAgTGLmAEaJ8mWLRoUXrDG96Q/1x33XXpsssuy5/Uvssuu6Sddtopxf5ey9FHH53ip98ST51/1atelf7sz/4sxVyh11xzTf5wg9/7vd9LD3vYw/odbj8BAgQIECBAgMCUCOh/TklDqgYBAgQIECAwMQICoLNoip133jnFT51LdHhHcZ466yBvAgQIECBAgACB4QiMol+o/zmctpILAQIECBAgMLkCvYcvTm65lYwAAQIECBAgQIAAAQIECBAgQIAAAQJ9BQRA+xJJQIAAAQIECBAgQIAAAQIECBAgQIBAUwUEQJvacspNgAABAgQIECBAgAABAgQIECBAgEBfAQHQvkQSECBAgAABAgQIECBAgAABAgQIECDQVAEB0Ka2nHITIECAAAECBAgQIECAAAECBAgQINBXQAC0L5EEBAgQIECAAAECBAgQIECAAAECBAg0VUAAtKktp9wECBAgQIAAAQIECBAgQIAAAQIECPQVEADtSyQBAQIECBAgQIAAAQIECBAgQIAAAQJNFRAAbWrLKTcBAgQIECBAgAABAgQIECBAgAABAn0FBED7EklAgAABAgQIECBAgAABAgQIECBAgEBTBQRAm9pyyk2AAAECBAgQIECAAAECBAgQIECAQF8BAdC+RBIQIECAAAECBAgQIECAAAECBAgQINBUAQHQpracchMgQIAAAQIECBAgQIAAAQIECBAg0FdAALQvkQQECBAgQIAAAQIECBAgQIAAAQIECDRVQAC0qS2n3AQIECBAgAABAgQIECBAgAABAgQI9BUQAO1LJAEBAgQIECBAgAABAgQIECBAgAABAk0VEABtasspNwECBAgQIECAAAECBAgQIECAAAECfQUEQPsSSUCAAAECBAgQIECAAAECBAgQIECAQFMFBECb2nLKTYAAAQIECBAgQIAAAQIECBAgQIBAXwEB0L5EEhAgQIAAAQIECBAgQIAAAQIECBAg0FQBAdCmtpxyEyBAgAABAgQIECBAgAABAgQIECDQV0AAtC+RBAQIECBAgAABAgQIECBAgAABAgQINFVAALSpLafcBAgQIECAAAECBAgQIECAAAECBAj0FRAA7UskAQECBAgQIECAAAECBAgQIECAAAECTRUQAG1qyyk3AQIECBAgQIAAAQIECBAgQIAAAQJ9BQRA+xJJQIAAAQIECBAgQIAAAQIECBAgQIBAUwUEQJvacspNgAABAgQIECBAgAABAgQIECBAgEBfAQHQvkQSECBAgAABAgQIECBAgAABAgQIECDQVAEB0Ka2nHITIECAAAECBAgQIECAAAECBAgQINBXYHHfFBIQGILAeeedl84888wyp/Xr16fVq1fn7xctWpQ233zzcl+svOlNb0pbb7112zZvCBAgQIAAAQIECBAgQIAAAQIECMxWQAB0tmLSz0kgAp4PPPBAeeyDDz6YLr/88vz98uXL0x577FHui5VWq9X23hsCBAgQIECAAAECBAgQIECAAAECcxEQAJ2LmmNmLbBs2bK0atWq8ri1a9emW265JX8f26v7YuOmm25aprVCgAABAgQIECBAgAABAgQIECBAYK4CAqBzlXPcrAT23XffFD/Fct9996Ubb7wxf/uIRzwive1tbyt2eSVAgAABAgQIECBAgAABAgQIECAwNAEPQRoapYwIECBAgAABAgQIECBAgAABAgQIEJg0AQHQSWsR5SFAgAABAgQIECBAgAABAgQIECBAYGgCAqBDo5QRAQIECBAgQIAAAQIECBAgQIAAAQKTJiAAOmktojwECBAgQIAAAQIECBAgQIAAAQIECAxNQAB0aJQyIkCAAAECBAgQIECAAAECBAgQIEBg0gQEQCetRZSHAAECBAgQIECAAAECBAgQIECAAIGhCQiADo1SRgQIECBAgAABAgQIECBAgAABAgQITJqAAOiktYjyECBAgAABAgQIECBAgAABAgQIECAwNAEB0KFRyogAAQIECBAgQIAAAQIECBAgQIAAgUkTEACdtBZRHgIECBAgQIAAAQIECBAgQIAAAQIEhiYgADo0ShkRIECAAAECBAgQIECAAAECBAgQIDBpAgKgk9YiykOAAAECBAgQIECAAAECBAgQIECAwNAEBECHRikjAgQIECBAgAABAgQIECBAgAABAgQmTUAAdNJaRHkIECBAgAABAgQIECBAgAABAgQIEBiagADo0ChlRIAAAQIECBAgQIAAAQIECBAgQIDApAkIgE5aiygPAQIECBAgQIAAAQIECBAgQIAAAQJDExAAHRqljAgQIECAAAECBAgQIECAAAECBAgQmDQBAdBJaxHlIUCAAAECBAgQIECAAAECBAgQIEBgaAICoEOjlBEBAgQIECBAgAABAgQIECBAgAABApMmMNIA6OrVq9M999wzaQbKQ4AAAQIECBAgMKUC+p9T2rCqRYAAAQIECBCYhUCtAdCbb745HX300emAAw5IO++8c9piiy3S+973vrx4V111VXrGM56RTj311LR+/fpZFFlSAgQIECBAgAABAp0F9D87u9hKgAABAgQIEFjIAovrqHyr1Uonnnhi+sAHPpDuuOOOjqf4zW9+k84555z851WvelX67Gc/m5YsWdIxrY0ECBAgQIAAAQIEegnof/bSsY8AAQIECBAgsLAFahkBesIJJ6QjjjgiD34uXrw47bfffmnPPfdsk163bl0Z8PziF7+YDj/88Lb93hAgQIAAAQIECBAYVED/c1Ap6QgQIECAAAECC09g6AHQiy66KB1zzDG55MEHH5yuuOKKdP7556dYry4HHXRQvi9ug48lRoBeeuml1STWCRAgQIAAAQIECPQV0P/sSyQBAQIECBAgQGBBCww9AHr88cen+++/Px/1+ZWvfCXtuuuuXYF32WWXdNZZZ6VtttkmPfjgg+kzn/lM17R2ECBAgAABAgQIEOgkoP/ZScU2AgQIECBAgACBQmDoAdALLrggzztGgS5fvrw4T9fXSFOMDr3sssu6prODAAECBAgQIECAQCcB/c9OKrYRIECAAAECBAgUAkMNgMYozosvvjjPe//99y/O0ff1ec97Xp7mmmuu6Zv2/7N3H+BRVGsDx18CSQihShOkSBNQxAIoTVFQBAtVEAuWC/JdxQJ4RbAiqKhX7F4V9XqtSFFRFERBQVFUVHoTEFABKdJbSPvmPTDDbthNtswmW/7neUKmnJk585tdcvbdU8iAAAIIIIAAAggggIAtQP3TluA3AggggAACCCCAgD8BVwOgxYsXl9KlS5tr7dq1y981j9m+detWs6169erH7GMDAggggAACCCCAAAL+BKh/+pNhOwIIIIAAAggggIAt4GoAVE962mmnmXPPnDnTvkaBv3UcUE1NmjQpMC8ZEEAAAQQQQAABBBDwFKD+6anBMgIIIIAAAggggEBeAdcDoGeffba5xsiRI2X16tV5r3fM+uuvvy7Tpk0z24PpNn/MidiAAAIIIIAAAgggkJAC1D8T8rFz0wgggAACCCCAQMACrgdA77rrLtHZ3ffs2SPNmzeXl156STZv3nxMgdavXy/9+/eXfv36mX1t27aVHj16HJOPDQgggAACCCCAAAII5CdA/TM/HfYhgAACCCCAAAIIuB4ALV++vLzxxhuSlJQkOg7oTTfdJMcff7wJhCr3uHHjpHLlynLiiSfKa6+9Jrm5uVKqVCnRlqB6DAkBBBBAAAEEEEAAgWAEqH8Go0VeBBBAAAEEEEAg8QQiEnE8//zz5fvvv5eWLVs6ohkZGWZ506ZNsm3bNmd7hw4d5KeffpL69es721hAAAEEEEAAAQQQQCAYAeqfwWiRFwEEEEAAAQQQSCyBEpG63RYtWsh3330nkyZNMr9XrVol+qMtPk866SRp0KCBaEW1S5cukSoC50UAAQQQQAABBBBIIAHqnwn0sLlVBBBAAAEEEEAgCIGIBUC1DMWKFZNevXqZnyDKRFYEEEAAAQQQQAABBEISoP4ZEhsHIYAAAggggAACcS3gehf4sWPHyqBBg2TZsmUBw40aNUpat24to0ePDvgYMiKAAAIIIIAAAgggoALUP3kdIIAAAggggAACCOQn4HoA9KOPPpJnnnlG1q5dm991vfbNmjVL5s6dG1TQ1OsErCCAAAIIIIAAAggkrAD1z4R99Nw4AggggAACCCAQkEBEu8AXVILs7GwzLujChQtNVp0NnoQAAggggAACCCCAQKQEqH9GSpbzIoAAAggggAAC0SsQVgD0kksukZkzZ3rdXWZmplnv3r27JCXl38BU8+bk5DjHN2/e3FlmAQEEEEAAAQQQQACBvALUP/OKsI4AAggggAACCCBQkEBYAdAxY8ZI06ZNxQ56el7M1zbP/XmXmzRpIt26dcu7mXUEEEAAAQQQQAABBBwB6p8OBQsIIIAAAggggAACAQqEFQBt1KiRvPjiizJv3jznctOmTZPff/9dOnfuLLVq1XK2+1pITk6W9PR0qVOnjvTu3VsqVKjgKxvbEEAAAQQQQAABBBAwAtQ/eSEggAACCCCAAAIIBCsQVgBUL9avXz/zY19YuyVpAHTgwIGiyyQEEEAAAQQQQAABBNwUoP7ppibnQgABBBBAAAEE4l8g7ABoXqK+fftK69atpWHDhnl3sY4AAggggAACCCCAgOsC1D9dJ+WECCCAAAIIIIBAXAm4HgDt06dPyEC5ublSrFixkI/nQAQQQAABBBBAAIHEE6D+mXjPnDtGAAEEEEAAAQSCEXA9AOp58XXr1slff/0lhw4d8prtXQOdWVlZkp2dLfv27ZPNmzfLp59+alqO3nPPPZ6nYBkBBBBAAAEEEEAAgYAFqH8GTEVGBBBAAAEEEEAgYQQiEgD9/vvvZdiwYTJ79uygIFu0aBFUfjIjgAACCCCAAAIIIKAC1D95HSCAAAIIIIAAAgj4E3A9ALpjxw7p3r27afnp76K+tlevXl10Vk8SAggggAACCCCAAALBCFD/DEaLvAgggAACCCCAQOIJuB4AfeSRR5zgZ4cOHaRLly6SlpYmAwYMkNTUVHn11VdNt/f169fLhAkTZM2aNVK3bl1ZsWKFJCcnJ94TSOA71iEQdHgEHRKBhAACCCCAAAIIhCpA/TNUOY5DAAEEEEAAAQQSQ8D1AOjPP/9s5Dp27CjTp093FB977DET7DzppJPkrLPOMtuHDh0qnTp1kh9++EHGjBljus07B7AQlwLz5s0zQfBPPvlENm7caO5RJ7763//+J127dpWbb75ZqlWrFpf3zk0hgAACCCCAQGQEqH9GxpWzIoAAAggggIC3gA71OHHiRJk2bZpp/JeUlCSff/65XHrppXLllVdK06ZNvQ9gLWoEktwuyapVq8wpb7/9dq9Tt2rVyqx/9dVXzvby5cvLjBkzpF69ejJy5EhZu3ats4+F+BLYs2ePXH311Sb4PXbsWCf4qXepLUAXL14sDz30kHktPP744/F189wNAggggAACCERUgPpnRHk5OQIIIIAAAgkvsHLlSmnfvr2cd9558sILL8hvv/0m+/fvl71798qSJUvk0UcfldNOO0369OkjW7duTXivaARwNQCamZkpGzZsMPfZoEEDr/tt2LChWV+0aJHX9tKlS0vnzp3lwIED8uGHH3rtYyU+BDZv3iwaAH/33XcLvCF9Hdx1111yzTXXSE5OToH5yYAAAggggAACiS1A/TOxnz93jwACCCCAQKQFZs6caRpzeTbo83fN8ePHS/PmzWX58uX+srC9iARcDYDqGJ4VK1Y0t1KihHfven8BUM3crl07c4y2AiTFl4CO8amTYi1dujSoG3vnnXfknnvuCeoYMiOAAAIIIIBA4glQ/0y8Z84dI4AAAgggUFgCGsvo1q2b7N69O+BL/v7776ah37Zt2wI+hoyRF3A1AKrFtWdyz9udvXHjxuZudLIjDYp5plKlSpnVYINknudgOToFnn/+eZk7d25IhdNxYxcuXBjSsRyEAAIIIIAAAokjQP0zcZ41d4oAAggggEBhCehwfddee63p5h7sNXXi70GDBgV7GPkjKBCxAOhLL73kVWxtAaqtQrOysuTrr7/22qcT4mgqU6aM13ZWYltAA90PP/xwyDeh/9mMGjUq5OM5EAEEEEAAAQQSQ8AOgFL/TIznzV0igAACCCBQGAIffPCB/PLLLyFfSnu20tAvZD7XD3Q9ANq3b1/RWb11VqzLL79cfvrpJ1No7Z7Upk0bs6wzfesM4BrgmjJlikyaNMlsr1+/vus3yAmLTmDWrFmyffv2sArw6aefmoGFwzoJByOAAAIIIIBAXAtQ/4zrx8vNIYAAAgggUCQC48aNC/u6OiYoKToEXA+AnnvuuXLrrbeau3v//felS5cuzp0OGTLELOtMnTVr1pRq1aqZ/fYMWVp5JcWPQKhd3z0FDh48SDd4TxCWEUAAAQQQQOAYAeqfx5CwAQEEEEAAAQTCFAhk0qOCLqENw0jRIeB6AFRva/To0XLLLbeIzvBer149504vu+wyGThwoFnXGb51dnA73XjjjdK2bVt7ld9xIKCtfN1Ibp3HjbJwDgQQQAABBBCITgHqn9H5XCgVAggggAACsSiQkZERdo9Wve8NGzbE4u3HZZm9p2p36RZ1UqPnnnvOjP+4YMEC56zaNV4nxWnWrJlMnjxZ5s+fL9rt/aqrrpL+/fs7+ViIDwEd9sCN5NZ53CgL50AAAQQQQACB6BSg/hmdz4VSIYAAAgggEIsC2dnZrhTbrfO4UpgCTqLzuNhDVNpZf/zxRzOEpa5rL++yZcvau6Rp06bSpEkTZz3aFyISALVvWmG0S1LedMMNN4j+kOJboEaNGq7coFvncaUwnAQBBBBAAAEEolqA+mdUPx4KhwACCCCAQEwI6Ber5cqVk127doVV3urVq4d1fGEerD21f/31V69L7tu3z1lft26dlCxZ0lmPtVhNRAOgjgoLCSnQvn37sO/7uOOOk9NOOy3s83ACBBBAAAEEEEAAAQQQQAABBBBAIFABHaZRJ2YOJ8X6UI+1atWSrKwsQ5CSkhIORZEfG7EAqI7b+O2338qaNWvk77//DuhGO3bsKBdeeGFAeckU/QItWrQwQxysXr065ML26dNHihcvHvLxHIgAAggggAACiSNA/TNxnjV3igACCCCAQKQFevXqFXYAVM8RKyk1NVVuv/12r+Lu2LFDdDxUTZUqVZISJY6GEdPS0rzyRvvK0ZK7WNJHHnnEjP+5f//+oM6anp5OADQosejOrGO+6muhd+/eIRVU30z33HNPSMdyEAIIIIAAAggklgD1z8R63twtAggggAACkRa4+uqrTWxr1apVIV3q0ksvFW0YFitJYzgVKlTwKm5ubq4TANV9ngFQr4wxsOL6LPA6KKoGrYINfsaAFUUMQUC/7bj22mtDOFLklVdekVgaLyOkm+QgBBBAAAEEEAhbgPpn2IScAAEEEEAAAQTyCGiw7/XXX5dQJmbW1pI6CTgpegRcbwH62GOPmbvTbsujR48W7cJcsWLFgF4wdHWOnheGmyUZO3asCYjnnU3M3zX0W4cnn3xS9NsWEgIIIIAAAgggUJAA9c+ChNiPAAIIIIAAAqEItGnTRt58803TsCszMzOgU2hLyY8//lhq164dUH4yFY6A6y1Aly9fbko+dOhQufPOO6VmzZqis2dpxLygn6Qk14tTOIpcJV8BHUdiwoQJ8tRTT0n58uXzzXvyySfLF198IYMGDco3HzsRQAABBBBAAAFbgPqnLcFvBBBAAAEEEHBbQBv2zZo1Sxo3blzgqXXSo3nz5kmrVq0KzEuGwhVwNeKYnZ3tlJ7JjBwKFiwBbdWpQc21a9fKq6++Kl26dJHKlStLmTJlRGcV69evn/mGZNGiRdKhQwfMEEAAAQQQQACBgASofwbERCYEEEAAAQQQCEOgdevWsnjxYnn77belW7duUrZsWXM2jXVUqVJF+vbtK1OnTpVvvvlG6tWrF8aVODRSAq52gdcu7Oecc45MmzZNdKYoEgJ5BbQFqAY7r7rqKrn33nvN7ho1asjgwYPzZmUdAQQQQAABBBAoUID6Z4FEZEAAAQQQQAABFwS0zqFD9emPBju196oGQHW9WbNmLlyBU0RSwNUWoFrQjh07mvJql2cSAggggAACCCCAAAKRFqD+GWlhzo8AAggggAACeQV0GEcNgJJiQ8D1AOitt94qPXv2lPHjx8vIkSMlIyMjNiQoJQIIIIAAAggggEBMClD/jMnHRqERQAABBBBAAIFCE3C1C7yWWpsEjxs3Ti6++GJ54IEHRGcAP+OMM8zsVyVLlsz3xvTbe/sb/HwzshMBBBBAAAEEEEAAgSMC1D95KSCAAAIIIIAAAgjkJ+B6AFQvpuMg/Pzzz+a6GzZsEP0JJJUuXZoAaCBQ5EEAAQQQQAABBBDwEqD+6cXBCgIIIIAAAggggICHgOsB0A8//NB0gc/NzfW4DIsIIIAAAggggAACCERGgPpnZFw5KwIIIIAAAgggEC8CrgdAP/jgA9Hgp3Z31zFAu3XrJtWqVZPk5OQCzUqUcL04BV6TDAgggAACCCCAAAKxLUD9M7afH6VHAAEEEEAAAQQiLeB6xHHWrFmmzCNGjJA777wz0uXn/AgggAACCCCAAAIJLkD9M8FfANw+AggggAACCCBQgICrAdCcnBzZuXOnuWSbNm0KuDS7i0pAn5OmLVu2FFUR5ODBg861s7Oz5a+//nLW42nBcyiI/fv3y4EDB+Lp9px78bzPXbt2ye7du5198bTgeZ/btm2TYsWKxdPtOffieZ+J8N48dOhQQvwftG/fPtH/h+Ixeb5mtR6i/w/FY8rMzHRuS/8P0r+fRZEyMjLMZe36RFGUwfOa1D89NaJ32X69FGX90/P/Cn0/JcLfOOqf0fueCLRknq9b6p+BqkVnPs9nSf0zOp9RMKXy/Gy/Z8+ehPibsnXr1iL7DOxG/dPVAGhSUpK0bdtWPvvsM1m2bJlZDuYFRN7CEbCDNunp6aITTxVF8hzuQF83RVWOSN97VlaWE/TUYSB0aIh4TPqfkf4R16T3GMiQF7HooH/k9JlqKlWqlOisw/GY9u7da4Yy0XuL1/emfhDXgKAmfY76POMx8X9QfD1V/XtpJ33NFtX70/4/3q5P2GUqqt/UP4tKPrjr2q+Xoqx/avBB/8Zp0teNliUeE//3x9dTpf4ZP8+T+mf8PEu9E8+YRkpKSpHVyyKtql+k2V+6699Nz/popK/teX436p+uBkC1cB06dDABUB2MfsCAAZ7lZTlKBKKhAuoZONLyFNWHuEg/Eg0M2t8M6Rs2Xu9THe0AaGpqatwGk7S1iGcA1P5PONKvo8I+vwYG7W+o4/U1q3/EPQOg8Xqf2tre/j8onitm+nq1/w/SL2HS0tIK+21TKNfz/NtJANSbnPqnt0c0rkVD/VODD3YAVN9P8fp/P/XPaHwHhF4m6p+h20XbkdQ/o+2JhFcez8+C+hk4Xv+maB3bDoBq/dMz8BueYHBH2952fSK4ow/nPtqUIJSjfRxz6623OkHQ3r17Ox8wfWRlEwIIIIAAAggggAACYQtQ/wybkBMggAACCCCAAAJxLeB6C9DVq1fLHXfcIUuXLpWJEyeKDkqv44HWqFFDypYtm29z2fbt28v5558f1+DcHAIIIIAAAggggIC7AtQ/3fXkbAgggAACCCCAQLwJuB4AHTp0qEydOtVx0kFSJ0+e7Kznt6BNaQmA5ifEPgQQQAABBBBAAIG8AtQ/84qwjgACCCCAAAIIIOAp4HoXeM+Ts4wAAggggAACCCCAAAIIIIAAAggggAACCBSlgOstQMePH+9MEhLsjcXrDNnBOpAfAQQQQAABBBBAIHAB6p+BW5ETAQQQQAABBBBIRAHXA6DxOvNVIr44uGcEEEAAAQQQQCAWBKh/xsJToowIIIAAAggggEDRCdAFvujsuTICCCCAAAIIIIAAAggggAACCCCAAAIIRFiAAGiEgTk9AggggAACCCCAAAIIIIAAAggggAACCBSdQMhd4L/88kuZMWOGKflFF10k7dq1M8tvvPGGrFy5MqQ76tChg+gPCQEEEEAAAQQQQACBvALUP/OKsI4AAggggAACCCAQiEDIAdBvvvlGRo8eba5Rrlw5JwA6YcIEmTp1aiDXPiZPamoqAdBjVNiAAAIIIIAAAgggoALUP3kdIIAAAggggAACCIQiQBf4UNQ4BgEEEEAAAQQQQAABBBBAAAEEEEAAAQRiQiDkFqDDhw+XIUOGmJvUlpt2mjRpkmRlZdmrQf32PE9QB5IZAQQQQAABBBBAIO4FqH/G/SPmBhFAAAEEEEAAgYgIhBwATUlJEf3Jm9LS0vJuYh0BBBBAAAEEEEAAgbAFqH+GTcgJEEAAAQQQQACBhBRwvQv82LFjZdCgQbJs2bKAQUeNGiWtW7d2xhQN+EAyIoAAAggggAACCCS8APXPhH8JAIAAAggggAACCOQr4HoA9KOPPpJnnnlG1q5dm++FPXfOmjVL5s6dG1TQ1PN4lhFAAAEEEEAAAQQSV4D6Z+I+e+4cAQQQQAABBBAIRCDkLvCBnLygPNnZ2bJq1SpZuHChyVqqVKmCDmE/AggggAACCCCAAAIhC1D/DJmOAxFAAAEEEEAAgZgVCCsAeskll8jMmTO9bj4zM9Osd+/eXZKS8m9gqnlzcnKc45s3b+4ss4AAAggggAACCCCAQF4B6p95RVhHAAEEEEAAAQQQKEggrADomDFjpGnTpmIHPT0v5mub5/68y02aNJFu3brl3cw6AggggAACCCCAAAKOAPVPh4IFBBBAAAEEEEAAgQAFwgqANmrUSF588UWZN2+ec7lp06bJ77//Lp07d5ZatWo5230tJCcnS3p6utSpU0d69+4tFSpU8JWNbQgggAACCCCAAAIIGAHqn7wQEEAAAQQQQAABBIIVCCsAqhfr16+f+bEvrN2SNAA6cOBA0WUSAggggAACCCCAAAJuClD/dFOTcyGAAAIIIIAAAvEvEHYANC9R3759pXXr1tKwYcO8u1hHAAEEEEAAAQQQQMB1AeqfrpNyQgQQQAABBBBAIK4EXA+A9unTJ66AuBkEEEAAAQQQQACB6Bag/hndz4fSIYAAAggggAACRS3gegA0kBvasGGDTJ8+XXJzc+Wyyy6TKlWqBHIYeRBAAAEEEEAAAQQQCEmA+mdIbByEAAIIIIAAAgjEhUBSJO4iKytLJk+eLF27dpUvv/zS6xIjRoyQGjVqmHFD+/fvL9WqVZNrr73WBEO9MrKCAAIIIIAAAggggECAAtQ/A4QiGwIIIIAAAgggkIACEWkBOnz4cHniiScMZ6dOnaR9+/Zm+ZNPPpGRI0d6Mefk5Mhbb70ldevWFQ2OkhBAAAEEEEAAgfwE3nvvPdm9e7eT5eDBg5KRkWHW09LSJCUlxdlXu3Ztueiii5x1FuJXgPpn/D5b7gwBBBBAAAEEEAhXwPUWoLNnz5Ynn3zSlKtEiRJSqlQpp4wPPfSQaelZvHhxGTx4sPz8889yyy23mP0aGNV1EgIIIIAAAgggkJ/Ali1bZOPGjc7PqlWrZNGiReZn7dq1znbNs3379vxOxb44EaD+GScPkttAAAEEEEAAAQQiJOB6C9DXXntNtFVnkyZN5P3335eTTjrJFH39+vXyww8/mOUePXo4QdIzzzxTli9fLjNnzpQ5c+ZIs2bNInSrnBYBBBBAAAEE4lHg77//Fg18atIWoGXKlInH2+Se8hGg/pkPDrsQQAABBBBAAAEExPUA6LJlywzrdddd5wQ/dcPUqVMd7p49ezrLutCrVy8TAP3ll1+8trOCAAIIIIAAAgjkFbj55pu9xg6fNm2afPXVVyabzgauX67aKSnJ9c4u9qn5HUUC1D+j6GFQFAQQQAABBBBAIAoFXA2A6qzuK1asMLeZd7wt/XCiSbu/d+zY0Szb/+hESJqWLl1qb+I3AggggAACCCDgU0CH2PFMum4HOnU5OTnZczfLcS5A/TPOHzC3hwACCCCAAAIIuCDgarMI7fp+6NAhUyzP7me6zW6Z0aJFC6lQoYJX0XUsL03ly5f32s4KAggggAACCCCAAAL5CVD/zE+HfQgggAACCCCAAAIq4GoAVFt36myrmuyWoLqsA9Pv3btXF33OxKrjf2qqVauW+c0/CCCAAAIIIIAAAggEIkD9MxAl8iCAAAIIIIAAAokt4GoAVClbtmxpREeMGCFr1qyR/fv3y/Dhwx3l3r17O8sHDx40kyGNHz/ebGvbtq2zjwUEEEAAAQQQQAABBAIRoP4ZiBJ5EEAAAQQQQACBxBXwHkTLBYfBgwfL22+/bWZ8b9y4saSmpjqtPzt16iQnn3yyucq3334rGgzduHGjWa9Zs6Zcc801LpSAUyCAAAIIIIAAAggkkgD1FaYx/QAAQABJREFUz0R62twrAggggAACCCAQvIDrLUB15tX//e9/ZgKCzMxMJ/h5yimnyHvvveeUcNeuXU7ws3LlymZfSkqKs58FBBBAAAEEEEAAAQQCEaD+GYgSeRBAAAEEEEAAgcQVcL0FqFJed9110qxZM5k6daqsXbtWzjvvPNHWn+XKlXOkGzVqJFWrVpWePXvK0KFDnbFDnQwsIIAAAggggAACCCAQoAD1zwChyIYAAggggAACCCSgQEQCoOrYpEkT8+PPtE6dOqYFaFKS641Q/V2S7QgggAACCCCAAAJxLED9M44fLreGAAIIIIAAAgiEIRCxAGhBZSpWrJjoDwkBBBBAAAEEEEAAgcIQoP5ZGMpcAwEEEEAAAQQQiD6BkAOgs2fPlq+++kq0Bef9998f1p2lpaXJoUOH5IEHHgj7XGEVhIMjJpCVlSUZGRnO+Q8cOCA6Rqwm3b5v3z5nny6UKlWKALmXCCsIIIBAwQI7d+4U/f/VTgcPHhQdc1uT/r/q+X9t8eLFpUqVKnZWfiMQEwLUP2PiMVFIBBBAAAEEEEAg6gRCDoDOmjVLHnzwQSlRooTfoOULL7wgn3zyiRnrUydG8peys7MlJydHcnNz/WVhe4wLzJ8/Xz799FPnLjT4OW/ePLNetmxZ0Q/pnum2226T4447znMTywgggAACBQjMnDlTFi9e7OTSL5/sgKhONJiamursK1OmjNxxxx3OOgsIxIIA9c9YeEqUEQEEEEAAAQQQiD6BkAOggdzK0qVL5bPPPmOCo0CwEiyPtjxq2LChuWv9UE5CAAEEEHBfYPfu3bJixQpz4mrVqomOv01CIN4FqH/G+xPm/hBAAAEEEEAAgeAFIhoADb44HBGvAtrS6MQTT3RuT1v82i08NRiq+z1TcnKy5yrLCCCAAAIBCNSsWdMr1/r162XdunVmW9WqVeXUU0919pcsWdJZZgEBBBBAAAEEEEAAAQQQiGcBAqDx/HSj6N4aNWok+mMnDYBu3rzZrGqws1KlSvYufiOAAAIIhChw1llnif7YSbvDb9iwway2atVKunfvbu/iNwIIIIAAAgggUCQC+jlw6tSpXtfWMcvtIfHKly/vta9Dhw5Sq1Ytr22sIIAAAsEKEAANVoz8CCCAAAIIIIAAAggggAACCCAQkoBOgqu9VDzT77//Ljo3SLFixbx6Dmqe/fv3e2ZlGQEEEAhJgABoSGwchAACCCCAAAIIIIAAAggggAACbgisXbvWTIyrw6Od6DF0mhvn5hwIIICAChAA5XWAAAIIIIAAAggggAACCCCAAAKFIlCjRg0ZOnSo17WysrJEJ2/U4dHy7ktNTfXKywoCCCAQigAB0FDUOAYBBBBAAAEEEEAAAQQQQAABBIIWSEpKklKlSnkdl5KSYoKfGgDNu88rIysIIIBAiAJJIR7HYQgggAACCCCAAAIIIIAAAggggAACCCCAQNQLEACN+kdEARFAAAEEEEAAAQQQQAABBBBAAAEEEEAgVAG6wIcqx3EIIIAAAggggAACCCCAAAIIIICAD4GtW7fKxo0bnT06y/327dvNunb1L1++vLNPF04++WQzDIDXRlYQQMA1AVcCoJs2bfJZoH379pnt+kb3l8fngWxEAAEEEEAAAQQQQCAfAX91S+qf+aCxCwEEEECg0ARWr14t06dPd66Xk5Mj9t8one0+71inderUIQDqaLGAgPsCYQdAdba26tWr51uyP//8s8A8+Z6AnQgggAACCCCAAAIIHBGg/slLAQEEEEAg1gQyMzPlxx9/NMUuV66cNG/ePNZugfIiENMCYQdAY/ruKTwCCCCAAAIIIIAAAggggAACCCDgskCtWrWkQ4cOzln37t0rv/32m1mvVq2a1z7dWLJkSScvCwgg4L5AyAHQxo0bS48ePVwtkZ6ThAACCCCAAAIIIICALwHqn75U2IYAAgggEI0CJ5xwguiPnXbv3i0zZswwq9rd/ZxzzrF38RsBBApBIOQAaO/evUV/SAgggAACCCCAAAIIFIYA9c/CUOYaCCCAAAIIIIBA/Akkxd8tcUcIIIAAAggggAACCCCAAAIIIIAAAggggMBhAQKgvBIQQAABBBBAAAEEEEAAAQQQQAABBBBAIG4FQu4CH7ci3BgCCCCAAAIIIIAAAggggAACCBSKwIYNG8zkQNu2bZPSpUvL+vXrpXbt2oVybS6CAAKJI0AANHGeNXeKAAIIIIAAAggggAACCCCAQJEL5OTkyNtvvy3PPPOM/PLLL17lGT9+vDRp0kQGDhwo/fv3lxIlCFt4AbGCAAIhCdAFPiQ2DkIAAQQQQAABBBBAAAEEEEAAgWAFNm7cKK1bt5brrrvumOCnfa4lS5bITTfdJGeeeaZpHWpv5zcCCCAQqgAB0FDlOA4BBBBAAAEEEEAAAQQQQAABBAIW+PPPP6VFixbyww8/BHTM4sWLTf6VK1cGlJ9MCCCAgD8B2pL7k2E7Agj4FViwYIHs37/f2a/L9rqO21OyZElnX/ny5eXkk0921llAAAEEEEAAAQQQQACBxBPIysqSrl27irYADSZt375dLr30UtHPIOnp6cEcSl4EEEDAESAA6lCwgED4Anv27JEDBw44J8rIyJCdO3ea9bS0NNF1OyUlJUmlSpXs1Zj6/d1338mWLVucMut9HTx40KzrfaakpDj76tWrRwDU0WABAQQQQAABBBBAAIHEFBg7dqzfLu8FiaxevVqefPJJue+++wrKyn4EEEDApwABUJ8sbEQgNIEvv/xS5s+f7xys33LaAdHk5GSvlpEaKLzrrrucvLG8oF1ZtFKi6ZRTTpEaNWrE8u1QdgQQQAABBBBAAAEEEHBZ4IknngjrjE899ZQMHz6cSZHCUuRgBBJXgABo4j577rwQBLRFqD1eTZUqVaRu3bqFcNXIX6J9+/ZOYFevNnfuXOei55xzjjRt2tRZL1u2rLPMAgIIIIAAAggggAACCCSegI7luXbt2rBufMeOHfLNN9/I+eefH9Z5OBgBBBJTgABoYj537jpCAtWrV5dDhw45Z9+wYYMza6F2d9fWkXbSFqGxmho1auRV9K1bt8qaNWvMtoYNG8oZZ5zhtZ8VBBBAAAEEEEAAAQQQSFwBndXdjaTnIQDqhiTnQCDxBAiAJt4z544jKKAzGuqPnVasWCF//PGHWW3ZsqX06tXL3sVvBKJCQIP07733nldZtOVybm6u2Za3Ba8OXF+/fn2v/KwggAACCCCAAAIIIJCfwObNm/PbHfA+z3kIAj6IjAgggIAlQACUlwECCCCQwALZ2dmiAU/PtGrVKtHxa3WiLm3R65l0OwkBBBBAAAEEEEAAgWAEypcvH0x2v3nLlSvndx87EEAAgfwECIDmp8M+BBBAIM4FNMhZsmRJr7vUb+gzMjKkePHictppp3nt0/wkBBBAAAEEEEAAAQSCEahTp04w2f3mjZc5FfzeIDsQQCBiAgRAI0bLiRFAAIHoF6hRo4YMGzbMq6A6ju2uXbtEx6nNu88rIysIIIAAAggggAACCAQg0Lp1a9HWm1rHDDVp3fSCCy4I9XCOQwCBBBcgAJrgLwBuHwEEEEAAAQQQQAABBBBAAIFICmjw8oYbbpCnn3465Mv06dNH8o5PH/LJONA1gZ07d8qBAwec8x08eNAJdJcqVUr27dvn7NMeZlWqVHHWWUCgMAUIgBamNtdCAAEEEEAAAQQQQAABBBBAIAEF7rnnHnnjjTdkx44dQd+9BtJGjRoV9HEcEHmBmTNnyuLFi50L6ZwBdkA0JSVFUlNTnX1lypSRO+64w1lnAYHCFCAAWpjaXAsBBBBAAAEEEEAAAQQQQAABHwI5OTnyxRdfeO3RySp1eCJN2oW8RImjH+F1PMwGDRp45Y/mlUqVKsmkSZOkY8eOohNxBpPeeustqV27djCHkLeIBHbv3i0rV640Vz/++OPFrfFfi+h2uGwcCRz93zOObopbQQABBBBAAAEEEEAAAQQQiB+BsWPHytatW50b0gkb7cCgtjDTlmZ20oDLVVddZa/GzG8NgM6dO9ervDpmpn2fFSpU8AqAarfyWAqA6o21b99epkyZItqdXQNlBSWdrFNbjfbo0aOgrFG9X1+vs2bNMi0l9Xlu27ZNFi1aJE2bNo3qcgdSuJo1a3plW79+vaxbt85sq1q1qpx66qnO/ryTrzo7WECgEAQIgBYCMpdAIF4FNm3aJJMnT5aJEyfKqlWrpFixYrJ8+XLp1auXdOnSRSpWrBivt859IYAAAggggAACCBSigHarzczMdK6oAaQtW7aY9RNOOEE0OGinYFsX2sdF4+/ffvvNuc9WrVrFxRiYnTt3NsE/7RL/7rvvSm5urk/6bt26yaOPPioNGzb0uT8WNv71118ycuRIefPNN73GwtSy6zYN1g8dOlT69+/vFdyOhXuzy3jWWWeJ/thJu8Nv2LDBrOprtnv37vYufiNQpAIEQIuUn4sjEJsC+k30fffdJy+++KJoZdQz/fHHHzJt2jRJS0uTIUOGiFZsdDnWklbENJjrmTy/gdcKt37rbift0sOA3rYGvxFAAAEEEEAAAXcFdOxAuyWknnnz5s2mFZ0u16hRQ8qXL6+LJpUuXdpejKnfOkHMNddc41VmbTGnDQ009ezZU7RFnZ08g772tlj5rd3Z3377bXnyySfl008/lddee010Mp309HTp16+fXHLJJaKB7VhOU6dONS1ddRgDf2nt2rVy0003yX//+1/TsKR69er+srIdAQTCFCAAGiYghyOQaAJaAdMKiV0R83f/OvD1ww8/LNOnT5ePP/5YqlWr5i9rVG7XlgMTJkzwKpvekx3w1YHYtZJqp3PPPdd06bHX+Y0AAggggAACCCDgnkDfvn29Tvb555+L1sc0ae+jli1beu2PxRXtTVW/fn2vousX7HbXf20tGG8BMr0/nR1eG1FoYwNtYDBgwAAvg1hc+eCDD+Tyyy/327o17z3NmzfPvIb1t2eQO28+1hFAIHQBAqCh23EkAgknoJWvCy+8UHRcl0DTTz/9JNrN5dtvvzXf6AZ6XDTm06Dv33//bYrWvHlz0ZYIJAQQQAABBBBAoCgFli1bJhs3bnSKoK0k7RZn2npQW9TZSSfQOe+88+xVfiOAQAQEfv31V9GAvb+u/f4uqUFgDZp+/fXXZmgxf/nYjgACoQkQAA3NjaMQSEgB/TY2mOCnjbRw4UK588475T//+Y+9Kep/JyUlSbt27bzKuX//fmdsHm1loN3e7cSslLYEvxFAAAEEEECgMAX0C9r58+c7l9QAqD25jAZAPbuD67BEBEAdKhYQiIjAsGHDRD83hJLmzJljeqFdccUVoRzOMQggkI8AAdB8cNiFAAJHBXRGSp3wKNSkM3fqmKB5u/WEer5IH6cB0PPPP9/rMjpGjz2ofps2beKuC5LXzbKCAAIIIIAAAjEpoMHPn3/+2ZRdx8Y85ZRTYvI+KDQCsSigrTg//PDDsIr+zDPPCAHQsAg5GAGfAgRAfbKwEQEE8groLIXhJA0c6iyP999/fzin4dgIC2RkZJhu/jrJk3bx13FPY3ESqwgzcXoEEEAAAQSiRuDss8+WRo0aOeXRWcP37dtn1k8//XTp1KmTs89z/HJnIwsIIOCawJQpU8I+lzY80aHHKleuHPa5OIF7AtrS/uDBg84JdcJce/iRRYsWeU3SphOUef6/7BzEQpEKEAAtUn4ujkDsCOhA8+GmL774ggBouIgROv6rr74S/bZZJ63y/MP+xhtvSIcOHWTgwIFm8qsIXZ7TIoAAAggggECIAscff7zoj5103MHjjjvOrOqEOQ0bNrR38RsBBCIssHLlSleuoOOIEgB1hdK1k3zzzTeyfft253yrV6+W33//3ax///33XkPF6f+7BEAdqqhZIAAaNY+CgiAQvQJakdbuHOEm+w9EuOfhePcEdJKE66+/XnSmSl9JxxGbNm2a+dEJsLQVr+fYp76OYRsCCCCAAAIIIIAAAokosG3bNldu263zuFIYTuJToGbNmlK1alWzT8dbJkW/AAHQ6H9GlBCBIhfIysqSzMzMsMsR6mDgYV+YE/gU2LFjh5x77rmyZMkSn/vzbtQWvC1atJBvv/2W8U/z4kTh+rp168wYVNqNSoeg0DHhatWqJc2aNYvC0lIkBBBAAAEEEPAnoEMSJScn+9vN9igSqFixoiulces8rhSGkxgB7RWnw4XZSYcasT/f6tBhnkHQ8uXL29n4HUUCBECj6GFQFASiVUArXNqVyrPJfyhlrVatWiiHcUwEBLRVb+/evQMOftpF0KBat27dTBCUiritEl2/v/76axk+fLh89913XgX78ccf5dVXX5XatWvLAw88INddd53oZF8kBBBAAAEEEIguAf2S+qWXXjJfZOqEVjk5OaaA77zzjlx88cVyww03HDNZZ3TdQeKWpkGDBq7cfKxMHOvKzcbISfJOKKc96fbu3WtKrwFP5k2I/gfJJ5/of0aUMEYFtOvwL7/8IjoOzNKlS2XhwoVmcpkYvR3T8i/cstPyLFxB944fN26czJgxI6QTzps3z1TKQzqYgyImoB+Ohg0bJu3atTsm+Ol50fXr18s//vEP6dy5s+zcudNzF8sIIIAAAgggUMQC//3vf6Vu3bpy9913i9a57OCnFuvvv/+Wt956S9q3b28CoZs3by7i0nL5vAKXXnpp3k1Br2uPK89xfYM+AQcggIBPAQKgPlnYiEDoAn/++afceOONZpzEq6++Wj799FMzsczDDz8sVapUMRPKzJ49O/QLFNGR3bt3D/vKPXr0CPscnMAdgUceeSSsE40ePVq0FSkpegRuueUWeeyxxwIukE5sdsEFF4h2qyMhgAACCCAQKwI6OYzWQ+677z4TDHz77bflzjvvlKeeesqZkCRW7iVvOfU++vXrF9AXlDpGe/PmzUU9SNEjUKdOHROcDqdEOvkoCQEE3BcgAOq+KWdMYAGtgGm3B+1mqk3i8yb9BvfLL7+U8847T/r37y/aSjRWknaXrVGjRsjFPf3008WNb0RDLkCYB+qz2rhxo5kM6q+//pKtW7eGecaiO3zVqlWmVXI4Jdi0aZP88MMP4ZyCY10U0NYgL774YtBn1G51GjglIYAAAgggEO0CW7ZsMcO36MzK2jpSh3bR+phunzNnjgwZMkTq1atn/q7puNexll544QV54okngiq2NrzQ+vWuXbuCOo7MkRV4/PHHJTU1NaSLaOvPvn37hnQsByGAQP4CjAGavw97EQhY4LnnnpPbbrst4PyvvfaaCaZpC9ESJaL/raiDOv/nP/+RLl26BHyPdkYdK1KPLVasmL0pZn7r0AWPPvqofPLJJ84YL1p4nQ39tNNOMzOo33TTTSFXcooCYv78+a5cVod4aNmypSvn4iShC2gLzrvuuivkE2hXu1tvvVX0SwoSAggggAAC0SiwaNEiueSSS0QDfvklnbhTA4na4EDr2NoaLxbShg0b5F//+ldIRdUvth988EF58sknQzq+KA7SxgQfffSR16W1u7/WaYoXLy4vv/yy176LLrpITjzxRK9t0byiY0Vqg5hgA5k6o/j777/PGO3R/HApW0wL0AI0ph8fhY8WAe3SPmjQoKCLo11QdbKSWEmXXXaZjBkzJqjiaiVGAyytWrUK6riizqwVaH2mGhR67733vIKfdtk0ODp48GDRlggaDIyVpK033UhaeSUVvYB+gAj3mepECyQEEEAAAQSiUUCDgxoAKyj46Vn25cuXS6dOnWKmZaS2GDx48KDnLQS1rEHfWOqdpD2rtO7i+aONLUqXLi3p6ele2zVPODZBQbqY+ZprrpHx48d7zQye3+mbNGkic+fOlZo1a+aXjX0IIBCGAAHQMPA4FAEV0HEQNVDmOUB5MDI6XlEsjd2j3YsmTJggFSpUKPA2ddb3zz77TLQCEEtJg59du3aVZ555JqBi68zobdu2lVmzZgWUv6gzaeXSjeTWedwoSyKfQ1snh5vcOEe4ZeB4BBBAAAEEfAnomJihfOmq9etQGij4KkMkt+lniUmTJoV1CQ0oTpkyJaxzFPXBtWvXNkMYxEqr3UC8evfuLRqM189C2iPOV6pcubJoAFxbwMbTvfu6V7YhUNQC0d/vtqiFuD4CBQjMnDlTFixYUEAu/7uzs7Pl2Wefleeff95/pijb06tXL7nwwgtNgFCDocuWLfMq4Zlnnil9+vQRHcC7VKlSXvtiYWXo0KEyderUoIqqXXZ69uxpWoJqBS6aU61atVwpnlvncaUwCXyS1atXh3332rpGW1do6wsSAggggAAC0SKgXy5Pnz495OK88cYbovW6xo0bh3yOSB/4xx9/mHHmw72Oth78xz/+Ee5pCuV4rUPee++9XtfSGe3tBiXaiMIzaY+yWE3adV/HatdWuhqkfuWVVyQjI0PUQIdP015ySUm0S4vV50u5Y0uAd1psPS9KG4UCH3/8cdilyjsGTtgnLIQTlC9fXh544AEzmY6OVaOTJF1//fWiAWGdWEVnsYzF4OfixYvl6aefDklw+/btMmzYsJCOLcyDzjnnHElLSwvrklpR0xnESUUvoK87N5Jb53GjLJwDAQQQQAABFdAJRsNJ2rpy3Lhx4Zwi4seGO4yNXUC3zmOfL9K/dQ6EQH9icR6BvH5ly5YVHU6sWbNm0rp1a9H6eJs2bQh+5oViHYEIChAAjSAup04MgbytH0O5ax3TaO/evaEcGhXHaFfoihUrynHHHReTQU9PRO2CopXlUJOOF7p27dpQDy+U47SVn7bQDSfpjKOVKlUK5xQc65KAdp1yI/E83VDkHAgggAACbgrMmDEj7NO5cY6wC5HPCfx1jc7nEJ+73DqPz5OzEQEEEIgDAQKgcfAQuYWiFXBrwPEtW7YU7Y1wddGxP90YCzEWWvTqbKGhtgLVbkiPPPIIr5goEdBJuMJN9erVk5SUlHBPw/EIIIAAAgi4JqBfSOsQLeEm7WIezalGjRquFM+t87hSGE6CAAIIRKEAAdAofCgUKbYEtCu4GymQSYXcuA7n8C+wfv162blzp/8MAe7R2eGjPekMk6+//npIxXzuuefklFNOCelYDnJfoEuXLmGfVLtkkRBAAAEEEIgmAf1iWn/CTTreYjSnKlWquFKvat++fTTfJmVDAAEEilyAAGiRPwIKEOsC2nIq3KTBTwKg4SqGf7wOvu5GCmWmUjeuG+w5rrjiCnnttdf8zkqZ93w6/tKYMWPkpptuyruL9SIUuOSSS6R+/fohl0DH39IJy0gIIIAAAghEk4B26XZjmJe8E+pE0z3aZbnqqqvsxZB+6zBUF110UUjHchACCCCQKAIEQBPlSXOfERPQsRDDTRrAIBW9QJkyZVwphFvncaUwBZxEZwvVWUPPPffcfHM2b95cZs+eLUOGDMk3X7Tu1CEmdHIuz59FixaJ/ixYsMBru+bJzMyM1ls5plwawAx14i492eDBg8MKoB5TIDYggAACCBSpwLZt20S/1NXu4/qlrD2zdpEWKsSLn3XWWSEeefSws88+++hKlC7pbODaEjTUdN9998X8OPyh3jvHIYAAAoEKlAg0I/kQQMC3wMUXXyw65o5OZBRq+uc//xnqoRznooB2C9dWjuFMgqTFqV27toulivypdDZKDW5qIPDjjz+WiRMnyu7du01F+vLLLzczVrZo0cLYRL40kbnCmjVrZPr06c7J9cPgvn37zLqOaVqqVClnny40aNAg4JaxXgcW0Yp+iTJy5Ei5//77gypBx44dZfTo0UEdQ2YEEEAAgegT2L9/vzz77LNm1vSlS5c6BRw/frz5+9C9e3cZNmyY1K1b19kXCwtaD/n000/DKmrPnj3DOr4wDtYJRd955x3p1KmTZGdnB3VJ/Vt+6623BnUMmRFAAIFEFKAFaCI+de7ZVQGdUfvRRx8N+ZxasWvTpk3Ix3OgewI6nmvLli3DPqFWXmMxnX766SaA1qtXL9HXZdeuXWXUqFGirS80MBxP6dChQ/L999+bnyVLlsTFrWnrj+eff140oBtIuv76603AO9D8gZyTPAgggAAChS/w9ddfm5b8w4cPF8/gp10SbRH6yiuviE6aF2tfel199dVhBW219WesdA2/4IIL5K233gpqUsLzzz9fJkyYEPDffvs1wW8EEEAgEQUIgCbiU+eeXRfQylko4yI2bNhQXn31VdfLwwlDF9Au4eGkOnXqSLt27cI5BcdGQODEE080rSo0OK0/+iFDW+rqT+PGjb326X79YiMWk47luWzZMtHxXVNTU33eQtu2beWLL74wk2D5y+PzQDYigAACCESdwIcffigdOnSQTZs2FVg2Hd7l7rvvlhtvvLHAvNGSQccBHTt2rCQlBf+xNS0tTV566aVouZWAynHllVfKt99+a758zu8A7bmivT4+//xzKVeuXH5Z2YcAAgggcESALvC8FBBwSUBbXqWnp8sTTzwR0BlbtWolWmml0hIQV6FluuGGG8x4ir5aUARSiMcee0x0TEZSdAnoBAiekyBoF/9p06aZQmrQ2o2Wv9FyxyeddJK89957pou/dqd7//33TXc6bcmrY4wdf/zx0VJUyoEAAgggEIbA4sWLRb+ED3amdP3yXb/8i5VxvTXAq4FMHTIq0PFMU1JS5N133xXt3RJrScdd/+GHH2TmzJnms8Jnn30mW7duNUPz6P1069ZNtLdO1apVY+3WKC8CCCBQpAJ8Si9Sfi4eTwL6zfS///1v05JMuyDNmzfP5+3pbJb33nuvaTGq32rHYtJv4rUiZqdVq1bJ6tWrzaqOq6gVNjvpWFP6bXasJO0OPGnSJNEuUxokCyZpK2CtkJIQiAYB/UJGv2hZuXKlKY6O4xpvwc89e/aIfjDUQO/y5ctNCyH9v0ln023fvn1Q3Qij4ZlRBgQQQCAYgdtvv10OHDgQzCFOXq2Lav3M88tBZ2cULmirVR2rvV+/frJx48Z8S1ivXj0zFmqsf7mpgV/9+e9//+sMbXDHHXdI9erV871/diKAAAII+BYgAOrbha0IhCygFZUff/xRfv31V1P50g/nOpi5ftOulTftfhrrY+5pSwPPWbJ11nOtlGrSrsOe+4JtlRAyvIsH6hhZ2kVYx8DU2VMDSTfffLM888wzgWQlDwIIhCmgk33oF076Y09mZZ9yxYoVZgw1nU13xIgR8n//938hdZ20z8dvBBBAIBoFtIXgV199FXLRNHCq9ZZwxrEP+eIhHqhD1OgX7i+//LKZsHHu3LnOxJVat9YhiPr06SM6xnWsNjIIkYbDEIgKAa2f6bAMOvmaNgbSz8CLFi2Sv//+W7p06SJaNyMhUJQCBECLUp9rx7WAdkPVVkj2h3P9FjpexobUgKdOImMnHWOpUqVKZlWXtduRnbQVWiwm7S48f/580Yll9Jt3f12u6tevL9rtvUePHrF4m5QZgZgT+OOPP8yXE/r+zC9t2bJF9IsJnT143Lhxov9vkRBAAIF4EdBhlMJNkydPjqkAqN6v1jMHDRpkfnQoG70HnajxuuuuM70ewjXheAQQCF5APyfpcHAjR440wU7PM6xbt05mzJhh6mTaW06/nK5QoYJnFpYRKDQBAqCFRs2FEIgfgb59+3rdzN69e0W7omrSMU11YPZ4SNpdWGdN1ZnQP/74YxNE0eCLtiq48MILTXf31q1bx3yL3nh4VtxDYgjs3LnTdAfUYTcCTRoA7d69u+kqz/i8gaqRDwEEol2goC+BAim/DpGiLUE1qBiLSVt9li5d2hRdg6AkBBAofAEdMkxbXttj6/srgfYQfPbZZ80X05988olojzsSAoUtQAC0sMW5HgIIxJyABkIHDBhgJjeyJ0diDKaYe4wUOA4E9H0YTPDTvmUdl/ihhx4yrQ7sbfxGAIH4F9Cul19++aVzo9pKyf7C1jN4ZmfQiRBjZXLKzZs328UO67cO9aOTAZIQQACBYAW0i3vPnj1NC89Aj12zZo0Zp/3nn3+OmTGIA7038kW/QFL0F5ESIoAAAggggECiC+h4dxMnTgyZQccLdStgEHIhOBABBApVICMjQ7TluP2zY8cO0XGC9UfHkrS327/1w3ysJLvlY7jldes84ZaD4xFAIPYERo8eHVTw077DTZs2Sd4ehfY+fiMQSQECoJHU5dwIIIAAAggg4IrA66+/HtZ5dGB+HZSfhAACiSOQlJRkhq3RoWv0R9d1KBv90XGC7e3271jqRl27du2wH6QOWWSP4R72yTgBAggklMC2bdvMPAih3rT2zpk+fXqoh3McAiEJ0AU+JDYOQgABBBBAAIHCFChobKlAyqLnuO222wLJSh4EEIgDgWbNmon+2EnHu9RxyzXVqFFDBg8ebO+Kud86I/q7774bVrkvuugiM4FQWCfhYAQQSEiB999/3/n/NFSA//3vf6L/D5EQKCwBAqABSG/YsEHeeust0YHCdblKlSrSpEkTOfXUU834FW4NHK7dbvTDmc6Spt9Ma2uVxo0bm+u0atWKgYIDeFZkQQCB4AR0LDR7XFP7SP3/Rz8g6oQx33//vb3Z/G7YsCEzN3qJsFIYAvr3UV+X4SadiZSEQKwIUP+MlSdVNOXs0qWL+Xus3fpDTTrmKSm6BHJzc+Xrr7/2KpR+BrX/Bs6dO9er1W6tWrUYw9VLi5XCEnCj9eZnn31WWMXlOggYAQKgBbwQxo0bJy+//LJ4jgm0fv160R+dWXbKlCmi44qVKVOmgDPlv3vr1q2ik6qsXbvWK+O8efNEf9544w0ZPnw435B46bCCAALhCugHp7yVDx2cXMdN066CefeVL1+eAGi46BwftMC+fftEPxSGm+zJT8I9T1Ecr/evM61qfSElJUUOHjxYFMXgmoUkQP2zkKBj+DI6WdN9990nQ4YMCekuzj33XLnssstCOpaDIiegnzm/+uorrwvo5H/a3ViTBkA9x23V58gkVl5crBSSgMZDwk06/rLWzcKNpYRbDo5PHAECoPk8aw1w/uc//zE59MNG+/btTctPHTPo22+/FQ0SaMupW265RZ566ik57rjj8jmb/136we7OO+90gp/169eXNm3aSOXKlWXBggXmW8BDhw6ZGWw1b48ePfyfjD0IIIBAmAJnnnmmK8GmMIvB4Qg4AmXLlpWSJUuGHfQ7/vjjnXPGyoJO1DJmzBiZPHmy6GzNdtJuY1ov+cc//iFXXHEF3VhtmDj4Tf0zDh5iId3C7bffLrNmzZKPP/44qCtWrVpV3nnnnaCOIXPRCTRo0MAJcrrV87Do7iaxrqx/t3///XfnprWHlU4ApEnHHP7xxx+dfbpwxhlnmLGJvTZG6YrGJdxIeh4CoG5Ico5ABAiA+lHSVlFPPvmk2Zueni46w5n+h2Qn7TLy0EMPiQ7e+9tvv4l+EAn1G1id2EGDqZrOP/98822uDsauqWvXrrJo0SIZOnSo6H8Ozz77rOg3fQxYbnj4BwEEwhTQL27ytgD5+++/nQBo3v9rYjGAFCYRh0eJwOmnn37MkAzBFu20004L9pAiy5+TkyMPPPCAPProo5KVlXVMObSV0BdffGF+NEA6YcIE5wPyMZnZEDMC1D9j5lFFRUG1p4a2FtbZlD/44IOAyqStBT/55BMzBmpAB5CpUAWKFy8ul19+udc1d+3aJdoYRpP2xLE/J+q6NpghRa+A9u707CquPazsHp/a+nHq1KlehW/UqJHX8/XaGWUr+plAh2cIJ2kQmNdwOIIcG6wAAVA/YhMnTnT+0Nx0001ewU89RMfGu//++82YoCtWrDDdRP/5z3+KzqYYTNLubPa3tjq2qHZl8fyjpudq2rSp2T5s2DDTFV/za4sPEgIIRFZAx+H1TFppyczMNJt0IgXP/fYMsp75Y2FZu1F5ThChZd68ebNo8EVTtWrVzG/+QaCoBbp16xZ2AFTPEQtJ33+9e/cWnWAgkPTTTz9J8+bN5csvv5RYCvIGcm+Jlof6Z6I98fDvVz97TJo0SV599VXz2cSzpbjn2fWzy8033ywjRoyI2aFsvvnmG9Nd1r6vX375xTRE0fXvvvtOtm/fbu8yQZUWLVo467GyoAEhnWvCM+kXI/awJ/rFdN7Pip55WY5uAX12rVu3NoXUYHcsJ20cNnv27LBuQessse4QFgAHF7oAAVA/5Nr9SJN2M7j44ot95tJvXbXb2YMPPigaDNGx8oLtnq5jvOixmvSDmb8/aNolvmbNmmYAbA2AXnvttSYI67NgbEQAgbAFtMXV448/7nWe+fPniw6BoUmHx/DsrqEts7U7KgkBBCIjMGDAANMaUltMhJL0A+Ull1wSyqGFfszdd98dcPDTLpx+8NfW3BoQyNty287D7+gXoP4Z/c8oGkuoQbMbb7zRfD7QL0LGjx9vAoLaUrxu3bpy5ZVXmv8fYr2l1eLFi516mD4H7VqsjUk0aUs0e1nX69WrJ7EYANWyk+JHQN9/nnUPfU9qQFuTfimhLXo9UywNcdC9e3d5+umnPYsf9LKeg4RAYQoQAPWhreNy2N8gFjQOh2fLqc8//zzoAOiSJUucEpx11lnOsq8Fbd2hMwBq91T9gFNQfl/nYBsCCIQuUL16dafVRGpqaugn4kgEEAhaoEKFCmbSQf2QH2zS1gXPP/+8mdgr2GMLO7+OLa6TK4aStI6grbv0XkmxJ0D9M/aeWbSVWOsmnTt3NsNhvPLKK6Z4LVu2lF69ekVbUV0pj3bB1b8NmnScaBIC0Sag4+3qj500AGo3ptA5RipWrGjvirnf2vhDG2np3CihJB3ffeDAgaEcyjEIhCxAANQHnX74sJNOSJRf0j+6+h+XBiV1LNBgk30t/eZWv6nML3mWRa9FADQ/LfYhEJ6Avidr167tdRJtVWV3gdeu4/rNrZ3yfoNrb+c3Agi4J9C/f3/RLw6feeaZoE76wgsvSLt27YI6pqgy65jj9hAUoZTh5ZdfNmOHxnpLr1DuPdaPseuEeh+edT5f90X905cK2+JdoEuXLs4QZXqvOj+CPRGL9srxbD0X7LBk8W7H/SEQCQH9wrVVq1bOEA3BXEN72sVyADiYeyVv9Agc/fQePWUq8pL8+eefThm0xVdBSb/V0QCodmXftm1bUF3PNmzYYE6vH1Q8gym+run57ZHnbHK+8rINAQTCE9AWYzrZmWdiDCZPDZYRKBoB7W5Vq1YtGT58uNcHYV+lKVeunOhEg7HSxUq/YJkyZYqvWwl4mw7foedgrPCAyaImI/XPqHkUFCRKBWrUqOFVMp1Re8+ePWab/n9P0NOLhxUEIi6gE1S++eabctVVV/mcsNFfAQYNGiT/93//52832xGImAABUB+09jeJusvuVuEjm7PJs+WXjj0T6NhbGjC1W3mEch2nAD4WdDZYnRTBVypxZHKTgzffJnsvu0Syunf1lc3ZljpkqFizvUiu9c3qoX+Pdrb7Wigx6UMpPmOm2ZU58CbJOfUUX9nMDNNJm/6S9EesMRaLWd/gNm8mmf/X32dee2Pyo09I0rp1ZjXjKat7oDU+q79U/OtvpMS7483urF49JbvD+f6yivW/taTeMsjsz61xghy6d7j/vNaeEv99Q4r/OM/kOXT3UMm1Poj7S9lLlsoV02aY3Zn7D8rOCy/0l9VsTxl+nxTTcWGSUyTjuSfzzVv8k6lSwvrRlNn/BsmxDP0ma2y41LvvN7tzTm4smbfl390g+alnJWnlryZ/xqMP6ZSTfk+d9MM8Kf7a61ImJ1cyLu0s+y+9ON+gROpAyzo7S3Kt1pSHHhrh97y6o8S770nxr+eYPIeG3C65JzXwm7/YuvWS8qj1urBSdsuzJev6vn7z6o6UBx+WYtZwF5oyXnzOeh1aL0Q/qfgXM6XE+x9KSnaOZF/ZSzJbtTQVbh0H+JhktUZIveMusznHGvcnc+iQY7J4bkh+cawkLVxkNh0acZ/kHn+0m4xnPl3WfJpfU/ZFFxb83tVyWOUJ6L1r3Z/ep6YkfT2dcrJZ9jXeYrGNmyRl5MNmf87pp0nmP280y/7+Ceq9+80cKfHOe+ZUBb53rW5EqQNvN3lzT6guh+67218RzHb7vav/9046pZHpsqOzquq4YfoN9Hnnnee0Him2YqWkPG29LqyU3e5cybqyt1n290+K9f4qppMvBPLe/XSalJhyeJzpzH6WdYsA37uNG0nm7bf4K4LZnmyVOckquyZ973pO1KXBNc/nmfTjT5L83/+ZvFmXXSrZl3Qyy/7+cd67ltWhhx/0l81sj+h7d+QjctPGjXJtx4vlXxXKyBRrzG572Bq7UPohuWfPnnLXaWdI+fHvywHrJ+uaKyW7bRs7y7G/Pd+71izJmXfdcWwejy3JL75ivScXmi0FvncXLZbk/7xs8mZ3vECyenTzONPRRXsMu+ekhJS2Nu+xfm6TY2eAP3qESG9Jks7Wj6anJVsWSq4Z+8/XmOTFrL+7KQ9a/6dbKaD37mNjJMmawVZTxpPW3+sjEz3qhHCa7DqMWeGfsAWof3oTRqL+qRPJlN2zVzrP+V7SSqXJvkPZ1D8tduqf3q+9iP4No/7pha3160Spf+qNZwy3PldXqWwM9AtLz3qZbozF+ueF1ufbBVdfK4vHTzAtQQdb9Zad5g6P/Ue7/Y/9Rz/pvWmrHOhzjcRa/bOYVf/UlPGfZ60PTElOz0Ddpn/D7fqR/dlRt8dC/VPLqcl8hrXuQ6yejhlPPHp4o/Wv3QNSN2i8Sz8De753Mwf+04r7eE/e5hxsLURT/bOEZ8FYPizgWQENZJw/zzz2DH2BWEbyOjpZy0cffeSzGE0bNzbb9QN4jtXC9UCnjj7z2RtLfTBZknbtlhzrQ++ukYeDaPa+vL/Tf/pZSo6fZDbvv6SzHKpfN28WZ7249eYpaf3R05Sxb78cuPZqZ5+vhdTpX0jygsMfNrUcub4yHdlWcvlKSTtSjgzrfg+0buk/txUAKX0kb5YVYDhwhxWgyyeVmfOtJH/4scmx94ZrJctqvesvFftrs5xplUXTKitAY0945S9/2sefSHEruJRbMlV2Pn44wOQvb6n5CyX5SLkPnHeuZBwJWPnKn/T3duceD7U7Rw7c+A9f2ZxtKTO/kuRv55r13UPvkJx8xrtMXbVaSk38wOTNrldH9luBOc//JJ2THllInzBJill/8LNr1pAD9xwOFObNY6+Xnvujc4/7eveUTOsYf6mE1Zo6/YhHthWoPHDF5f6ymu0lNQhllV3TzjGPmT9iZsXHP2lLljqvp0NntzABUPsPXN7sxaz3iv16ymx2hhy49aa8WbzWk2d/I8nWa1vTHuuPR3a5sl77PVdSflsrpY7cY6b1fgzovbtzl+Qcd1zB7915R9+7SRdbgbAjrydfr9niW7c61hm798iB667xLOYxy17v3QetIO8xOY5uKLl8hWNd4HvXCujZ1lmNGsqBfw0+eiIfSzlTp0nVmbMk2do39uMP5ecjJdH/L3XiEe06169fP7nllluk4h9/OveYVbKkHOh2mY8zHt1k3rsbNkqu9V4p+L27wHldH7CCqxlNDgebj57t6JLXe/fctnJgQL+jO30smffunO/MHn3ver4Xddwpz+dp3rtHXk+HatWUA+3z7yKePvF9KWaZZ9c4QQ7cO8zH1Y9uKv29x3vX+hIq3/euVZl03rtZVhkDee/+ukrKW5cb/cdqeeiRR8yEQXPmzDEVsq5du8r55x/+0ivtpVcc64PNzhT98ZeKWa9l+/Vk3ru33ewvq9mePPvro+/dm4N873a+yOe57d4dvayAZkXr28Ft1mv0Np85j24808p3jRyeSXaS5JgA6EbL1PNZ27m937u7A3vvzl9gDt+lX9Ac+aLI3/9/9nX4HZpAJOuFniWK5HWivf6pr90068eum2VUqUr903pxUP/0fIdYn/2pf3qBUP/04tBokFNfCKT+Webb7yT5g8Ofy/dqA40jAVD9EjHv3+rkGK1/1lmzVhoezLSgisvr9U6Ur9cc/oxly2lPVp0M6uabb5Y638+T5CONj2Kx/qn3tPMJKzho9Rb0TFrntuvdnp8dY6H+ad9HqQ8/kqQdO63PjhXkwKgH7M1ev+06YPpPvxyN+1j12kP1/Q/nGE31TwKgXo/z8Ipnixn9lqKg5JnHfkEUdIzuL6zrBFIW8iCAAALxLPD222/L8dbMuFceaSnn6161EqpjGemXR5NvH2wCbL7ysS26BHS4Cm3xqd3iNQUydE103cHR0niOX3d0a/BLbp0n+CtzRDgChVUvLKzrhGPBsQgggAACsSmgPVHnbdwg48ePF/3yXccI1V45Or8CCYGiFiiWa6WiLkS0Xf+hhx6S6dOnm2I999xzomNb5JcefPBBmTFjhsny0ksvySmnnJJfdmefttC44oorzLrONv/ss1ZT6nyStgy5+urDrSR1AqQxY8b4za0zwep4hb7S4gULZOTDD8sXT4yR47WsNU7wle3oNm39YbXYk2Sr3ZTV3TXfZH1rJX/9dTiLdle2xuPxlfQbrx1WS8cSK1ZYX54Ul9I1a1oj/vv/1sCcY9kKq2353sOnO/OMY7518brOlq0i69cf3qQfiqtW8drttaJvAavlqknavS+flpQmz5rfxOpveTi/5tVj/KQ1Vsuy6c8c7kZbr3UruWjAjX5yHtlsdZO02s4fbo2YT0slk9tqbSZHmuFLPaulrdXKz2+yWrnKkW7WYs24Jw1P8pvV7FhhdX/fs/twHn3m+uz9JWv82wyr1Z62fs6pVk3SrLLkOxOnWqu5tipteqq/sx7ebnVrF6u1oUlWCz+xhmHwm7S5/rLlh3dbrSOlbh2/Wc0Oq1Wn9bXr4Twtmuef12rJK9Z7Sj80Hqx2vORa59expnyO22v9oZdf5h8+X2mrvI2tcueXtBXqzp2Hc5xqdR2wWhv6TZrvSKtVsWY+lZo1/GY1O0J87+6sXEmyj1j7HJxc3dRPk86+6uZ7d+s2kXXrzKmtiJYr712dFOa2224TfUVUslrLaVpqtazbb5Z8/3OC9T6Z/fJYqan/N1Wx/v+ofTi45ju3tTVa3rs6dIXVut4k67279NdfRYO/mlq3bi2XXXaZWTb/WO9dsVoVm3SC9XegerWj+3wt/fyL9nmOyvfu559/Ll999ZUptf5ddf5uH3nvmh0nnihivbb9Js/3bnppkZMb+c1qdkTgvav1gjpW9/szrNepfkOtbSkW5Ntm2voTbuWpduR1/auVd5e1PnToUBk1apS1lCdZ/0/L4iWHN4bx3tUve5s3by5aX4rX2aXzyBXKKvXPPMwh/g2TfOqf+kXXI/fdL9W2/W39115FLtd6mZt/w6h/ej9E6p/eHrpG/dPbxOOzY7zVP50b1bqW1rmslGN9ntmh7wsr6ecI/TzhlbQOd2QYMuqfR2SiuP6pnw3tVrw6Qa7TMzjG6p/Oa9DP313t9m63btXhHzV+Ix7vXWlQP98h86xAQdTUP7V+Tcoj4NlyQseHKyh5tvpMT08vKLuzP9jreJaloOvoh3bzwd252tGFLVu2mJUUK+iTokGUgtLZZxWU4+h+DcTpTwHJjBuWVlKyzjhdilnBtRRrPMgC0+lNC8ziZNCgbkGBXSeztdCmteda/suN/X8o/tUKNvxlB4Cts6y3grBzs/UjrMiuv7dJue+/N8v6j/7R04CEV2rezGs135U6J4roTyBJWzIHc49NrUBcoMkKeh6ygmVZe/aYI/S+PFtFH3MaKxAccNIPMfoTSAr2HjWIHmiyugeL9bPP+lIhV/8Dt1Ky9brVH58pGOuCAu6eF9BAnP4EmkJ87+ZutgK+Guiyks9nGax1MO9da6gI0Z9AUwHWP//8swwePNicTUN9awsIJtmX3WD9oe8yaqQstMZ49BnotjPav6PlvatBdI/kWXYdr8freVrvXdGfQJM19m3AqZDfu57j8WqlzLnPI+/dgMtdwOvJ6zwReO+eaAVp9UvU+UuPfMHgdUHfK39am//M87ru3LnzUQPPw1x679rfndOawxM3/OVg64XUPz3MA6x/amukrOQS8ke1qpJrtRxPscZFLzAF8zesiOqfee+hmBVU0XvUdILVCMD5PzFvRns9Wv6GUf+0n8jh39Q/vT1c+hvmfdIjay7XP72uoY04jiT9P0iOfBY/pl6mefQzcSCfi+0TRst7N0Hrn55/h70+A8dY/dN+OYmfz46e9Wz9/Gs+XwT4d9ec26X3rhv1TwKgztM+uuAZXPQcJ+loDu8lz65Ensd65zp2zXOmwkhe59grsyVSAsuXLxcd/8pOf1vf9mmLHk36QdF+0+q6ftA5JgCqO0gIIOCawJ133mm634RywmXLlsmrr74q//znP0M5nGMQCEng2muvlbvuuiukY/UgDaKec845IR/PgUUn4FmHjGS9kPpn0T1jrowAAggggAACRSeQVHSXjt4re44fZreWzK+0dh5tdVJWuxcHmLSJtN291D5Hfodu1lZZR9Jx+XV3tjPxu8gFtFuDDlegP/qhlIQAAoUnoC2y7W7RoV5Vu8+TEChMgVtvvVVO0CEJQkyPWJNCma5JIR7PYUUnQP2z6Oy5MgIIIIAAAgjEvwAtQH08Y89A1QZrZun8UpY1Nqbd5blu3bpHx33I7yCPfTrWl7YS1FakOmZnBR2Ty0/680/t6HY4NT4yk7u9zu/oEGjWrJno68BOOmzBrl06IpsO7VjSGsLSGhPySOIDqi3BbwQiI6Azu4ebFlhjJuvfgXACUuGWgeMTS0B7B0ycOFHOO+888Rz6JhCFG264Qa688spAspInCgWof0bhQ6FICCCAAAIIIBA3AgRAfTzK+vXrm7H9dKBX/fCbX9IukvaAsCefbE2IE2TSY3766SdzlI41px94/KVFixY5u0K5lnMwCxET0JmI9cdOOi7I9iMTJmmXs2MGurYz8hsBBFwXWLNmjSvn1PMQAHWFkpMEKKAzpk6ePNlMlLjnyPjKBR2qXed1IkZS7ApQ/4zdZ0fJEUAAAQQQQCD6BegC7+MZaaDq7LPPNnvWrl0r2o3SX7Jni9f9oYzn6Bnw9DxX3utp93d7bMmGDRtaE34flzcL6wgggAACHgLaqt6NtHPnTjdOwzkQCEpAJzLSL0gvvfTSfI/TbtOvv/66vPHGGwVPdJLvmdhZ1ALUP4v6CXB9BBBAAAEEEIhnAQKgfp6u5weOf//73+KrBcbcuXPF7mKpXdm1xUbepIHLFStWmB+7q7xnngYNGogGNDXNmTNHfAVBtRXho48+6kzkcfXVV3uegmUEEEAAAR8CVapU8bE1+E2VK1cO/iCOQMAFgZNOOkmmTJkiS5YskVGjRsmZZ54pGvDUrtLXX3+9fPTRR6ItlHWZFB8C1D/j4zlyFwgggAACCCAQfQJ0gffzTNq0aWNagf7www8meHnbbbfJLbfcIk2aNJHdu3fLrFmz5IUXXjBBSZ3dW2cJ1t9501tvvWU+oOh2rdT6mtl18ODBzizDDz/8sBlT9MILLxT98K5d7MeOHSvaPV6Tdn1v166dWeYfBBBAAAH/Am4MFZKUlOR8SeX/SuxBILICp5xyiuiP1gtWrlxpLjZs2DAhOB9Z96I4O/XPolDnmggggAACCCCQCAIEQPN5yvfff7+MGDFC5s2bJ6tXr5ZBgwaZmVWzs7O9jtLAaCjd3+2T6IeaBx98UEaPHi0HDx6UV1991fzoJDme19Ix6B577DHRD+QkBBBAAIH8BTxbUuWf0//ec845hyFH/POwBwEEIiBA/TMCqJwSAQQQQAABBBJegEhaPi+BsmXLyhNPPCF9+/Z1Zu/2DEjqbN+PP/649O7dO5+zBLarffv28vLLL5uWRnaA075WiRIlpFevXmZ/+fLlAzshuRBAAIEEF6hWrZpcddVVYSkMGTIkrOM5GAEEEAhWgPpnsGLkR7sVi+oAAEAASURBVAABBBBAAAEEChagBWgBRhqMHDBggPnZuHGjrFq1SlJTU6VmzZqiH67tYKW/0/zrX/8S/QkkaUBVW39qK1C9jo4fqmN91apVS0qXLh3IKciDAAIIIOAh8Mgjj5ixmnft2uWxNbDFiy66SLp06RJYZnIhgAACLgpQ/3QRMwFONXPmTFm+fLlzpzrvgD15qk7kp58p7KSfY2688UZ7ld8IIIAAAggkjAAB0CAetQYj9SfSqWTJknLqqaean0hfi/MjgAAC8SxQu3ZtmTBhghmDOTMzM+Bb1clnxo0bF3B+MiKAAAKREqD+GSnZ+Dnv3r17Zdu2bc4NHThwQOxeYzpHgee+tLQ0Jx8LCCCAAAIIJJIAXeAT6WlzrwgggEACCnTs2FG0dUygE8Z06NBB5s6dKxUqVEhALW4ZAQQQQCDWBTTIWadOHfNTqVKlWL8dyo8AAggggIArArQAdYWRkyCAAAIIRLOATmakQ4vouM2vvfaaV3dAu9xnnnmmDB061IzrrC1mSAgggAACCMSCQNeuXUV/7JSRkSHbt283q6VKlZJy5crZu/iNAAIIIIBAwgoQAE3YR8+NI4AAAokloB8AH374YXnooYfkm2++MZPc6YdEHX95+PDhZrzlxBLhbhFAAAEEEEAAAQQQQACBxBAgAJoYz5m7RAABBBA4IqCtO08//XRp0KCB2aLdBHWyORICCCCAAAIIIIAAAggggEB8CjAGaHw+V+4KAQQQQAABBBBAAAEEEEAAAQQQQAABBCwBAqC8DBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgbgUIgMbto+XGEEAAAQQQQAABBBBAAAEEEEAAAQQQQIAAKK8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhbASZBittHy40hgAACCCSawOzZs+W3335zbvvPP/+UJUuWmHWd8X7nzp3OvrS0NOnTp4+zzgICCCCAAAIIIIAAAgggEK8CBEDj9clyXwgggAACCSewbds2Wb9+vXPfO3bskJSUFLN+4MABr31lypRx8rGAAAIIIIAAAggggAACCMSzAAHQeH663BsCCCCAQEILaJDzpJNOMgZ2IDShQbh5BBBAAAEEEEAAAQQQSEgBAqAJ+di5aQQQQACBeBS47LLLpHPnzs6tHTx4ULQVqKb09HQpW7ass69YsWLOMgsIIIAAAggggAACCCCAQDwLEACN56fLvSGAAAIIJJSAtvL0bOmZlJQkOvanplKlSpmfhALhZhFAAAEEEEAAAQQQQAABS4BZ4HkZIIAAAggggAACCCCAAAIIIIAAAggggEDcChAAjdtHy40hgAACCCCAAAIIIIAAAggggAACCCCAAF3geQ0ggAACCCCAAAIIIIAAAggggAACCAQtsG/fPjl06JBz3O7du0XHode0d+9eZzx6XdfhmcqVK6eLJAQKXYAAaKGTc0EEEEAAAQQQQAABBBBAAAEEEEAg9gU+++wzWbx4sXMjmzdvlgULFpj1bdu2yZo1a5x9ZcqUkTvuuMNZZwGBwhQgAFqY2lwLAQQQQAABBBBAAAEEEEAAAQQQiFOBsmXLysknn2zuTgOeJASiRYAAaLQ8CcqBAAIIIIAAAggggAACCCCAAAIIxJBAtWrVJCMjwymxdofXbvGaUlNTpVSpUs6+tLQ0Z5kFBApbgABoYYtzPQQQQAABBBBAAAEEEEAAAQQQQCAOBFq3bi36Yycd/3PHjh1mNT09XbRFKAmBaBBgFvhoeAqUAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQiIkAL0IiwclIEEEAAgWgS0MHXPQdn379/v6xevdoUUWeqnDx5sldxO3XqJCVLlvTaxgoCCCCAAAIIIIAAAggggEBsChAAjc3nRqkRQAABBIIQ2LJlizMbpR6mYxNpEFTT9u3bvfbptvbt2xMAVQgSAggggAACCCCAAAIIIBAHAgRA4+AhcgsIIIAAAsEJlChRQpo2bWoOKl68eHAHkxsBBBBAAAEEEEAAAQQQQCCmBAiAxtTjorAIIIAAAqEInHrqqVKrVi3n0OzsbNm2bZtZT0lJkQoVKjj7dKF06dJe66wggAACCMSuwKZNm+Sdd96RKVOmyPz5881sxeXKlZM5c+ZI165dpXfv3rT6j93HS8kRQAABBBAISIAAaEBMZEIAAQQQiGUBDWh6BjU1AJqcnGxuSQOgFStWjOXbo+wIIIAAAj4E9P/6UaNGyeOPPy4HDhzwyrF161b54IMPzM+9994rTz/9tPTo0cMrDysIIIAAAgggED8CzAIfP8+SO0EAAQQQQAABBBBAAAFLYN++fdK5c2d58MEHjwl+5gX6448/pGfPnjJixIi8u1hHAAEEEEAAgTgRIAAaJw+S20AAAQQQQAABBBBAAAGR3Nxc6du3r3zxxRdBcWiw9MUXXwzqGDIjgAACCCCAQGwIEACNjedEKRFAAAEEEEAAAQQQQCAAgTfffFM+/PDDAHIem2XQoEGybt26Y3ewBQEEEEAAAQRiWoAAaEw/PgqPAAIIIIAAAggggAACtkBOTo7cd9999mrQvw8dOmTGDQ36QA5AAAEEEEAAgagWIAAa1Y+HwiGAAAIIIIAAAggggECgAnPnzhUd0zOcNGnSJMnMzAznFByLAAIIIIAAAlEmQAA0yh4IxUEAAQQQQAABBBBAAIHQBGbPnh3agR5H7d69WxYuXOixhUUEEEAAAQQQiHUBAqCx/gQpPwIIIIAAAggggAACCBiBDRs2uCLh1nlcKQwnQQABBBBAAIGwBQiAhk3ICRBAAAEEEEAAAQQQQCAaBHQMUDeSW+dxoyycAwEEEEAAAQTCFyAAGr4hZ0AAAQQQQ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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_2_updating.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">9</span>, <span class="at">height =</span> <span class="dv">3</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
</section>
<section id="online-appendix" class="level1">
<h1>Online Appendix</h1>
<section id="figure-a1-natural-disasters-across-africa-1970-2020" class="level2">
<h2 class="anchored" data-anchor-id="figure-a1-natural-disasters-across-africa-1970-2020">Figure A1: Natural Disasters across Africa 1970-2020</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Total Number of Disasters by country</span></span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>emdat <span class="ot">&lt;-</span> <span class="fu">read_excel</span>(<span class="st">&quot;1_input/emdat_public_2020_12_11_query_uid-GZ254X.xlsx&quot;</span>)</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a><span class="co"># change name </span></span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(emdat)[<span class="fu">names</span>(emdat) <span class="sc">==</span> <span class="st">&#39;Disaster Type&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;Disaster&#39;</span></span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a><span class="co"># keep most common disaster (scale gets to messy otherwise)</span></span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a>emdat <span class="ot">&lt;-</span> emdat[ <span class="fu">which</span>(emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Flood&#39;</span>  <span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Drought&#39;</span> <span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Storm&#39;</span> <span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Wildfire&#39;</span> <span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Insect infestation&#39;</span><span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Extreme temperature&#39;</span>), ]</span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a><span class="co"># subset, 1970 +</span></span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a>emdat<span class="ot">&lt;-</span>emdat <span class="sc">%&gt;%</span> dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="fu">as.numeric</span>(Year) <span class="sc">&gt;</span> <span class="dv">1969</span>)</span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a><span class="co"># create event variable for disaster</span></span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a>emdat<span class="sc">$</span>total <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="dv">1</span>,<span class="fu">nrow</span>(emdat)) </span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a><span class="co"># aggregate by group year </span></span>
<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a>DisasterAggTypeYear<span class="ot">&lt;-</span>emdat <span class="sc">%&gt;%</span> </span>
<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Year, Disaster) <span class="sc">%&gt;%</span> </span>
<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(total), <span class="fu">list</span>(<span class="sc">~</span><span class="fu">sum</span>(.)))</span>
<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a>fig_disaster_time<span class="ot">&lt;-</span><span class="fu">ggplot</span>() <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">y =</span> total, <span class="at">x =</span><span class="fu">as.numeric</span>(Year), <span class="at">fill =</span> Disaster),</span>
<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a>                      <span class="at">data =</span> DisasterAggTypeYear,</span>
<span id="cb17-27"><a href="#cb17-27" aria-hidden="true" tabindex="-1"></a>                      <span class="at">stat=</span><span class="st">&quot;identity&quot;</span>) <span class="sc">+</span> </span>
<span id="cb17-28"><a href="#cb17-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">1970</span>, <span class="dv">2020</span>, <span class="at">by =</span> <span class="dv">10</span>)) <span class="sc">+</span></span>
<span id="cb17-29"><a href="#cb17-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb17-30"><a href="#cb17-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis_d</span>() <span class="sc">+</span></span>
<span id="cb17-31"><a href="#cb17-31" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_fill_grey()+</span></span>
<span id="cb17-32"><a href="#cb17-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb17-33"><a href="#cb17-33" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.justification=</span><span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb17-34"><a href="#cb17-34" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.margin=</span><span class="fu">margin</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>),</span>
<span id="cb17-35"><a href="#cb17-35" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.box.margin=</span><span class="fu">margin</span>(<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>))<span class="sc">+</span></span>
<span id="cb17-36"><a href="#cb17-36" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">y =</span><span class="st">&quot;Number of Disasters&quot;</span>)<span class="sc">+</span></span>
<span id="cb17-37"><a href="#cb17-37" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Africa: Disasters&quot;</span>)</span>
<span id="cb17-38"><a href="#cb17-38" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb17-39"><a href="#cb17-39" aria-hidden="true" tabindex="-1"></a><span class="do">##################################</span></span>
<span id="cb17-40"><a href="#cb17-40" aria-hidden="true" tabindex="-1"></a><span class="co"># Malawi data</span></span>
<span id="cb17-41"><a href="#cb17-41" aria-hidden="true" tabindex="-1"></a><span class="do">##################################</span></span>
<span id="cb17-42"><a href="#cb17-42" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-43"><a href="#cb17-43" aria-hidden="true" tabindex="-1"></a>Malawi <span class="ot">&lt;-</span> emdat[ <span class="fu">which</span>(emdat<span class="sc">$</span>Country<span class="sc">==</span><span class="st">&#39;Malawi&#39;</span>), ]</span>
<span id="cb17-44"><a href="#cb17-44" aria-hidden="true" tabindex="-1"></a>Malawi_flood <span class="ot">&lt;-</span> Malawi[ <span class="fu">which</span>(Malawi<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Flood&#39;</span>), ]</span>
<span id="cb17-45"><a href="#cb17-45" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-46"><a href="#cb17-46" aria-hidden="true" tabindex="-1"></a><span class="co"># create event variable for disaster</span></span>
<span id="cb17-47"><a href="#cb17-47" aria-hidden="true" tabindex="-1"></a>emdat<span class="sc">$</span>total <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="dv">1</span>,<span class="fu">nrow</span>(emdat)) </span>
<span id="cb17-48"><a href="#cb17-48" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-49"><a href="#cb17-49" aria-hidden="true" tabindex="-1"></a>Malawi_agg_flood<span class="ot">&lt;-</span>Malawi_flood <span class="sc">%&gt;%</span> </span>
<span id="cb17-50"><a href="#cb17-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Year) <span class="sc">%&gt;%</span> </span>
<span id="cb17-51"><a href="#cb17-51" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(total), <span class="fu">list</span>(<span class="sc">~</span><span class="fu">sum</span>(.)))</span>
<span id="cb17-52"><a href="#cb17-52" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-53"><a href="#cb17-53" aria-hidden="true" tabindex="-1"></a>Malawi_agg_year<span class="ot">&lt;-</span>Malawi <span class="sc">%&gt;%</span> </span>
<span id="cb17-54"><a href="#cb17-54" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Year) <span class="sc">%&gt;%</span> </span>
<span id="cb17-55"><a href="#cb17-55" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(total), <span class="fu">list</span>(<span class="sc">~</span><span class="fu">sum</span>(.)))</span>
<span id="cb17-56"><a href="#cb17-56" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-57"><a href="#cb17-57" aria-hidden="true" tabindex="-1"></a><span class="co"># fill time series gaps</span></span>
<span id="cb17-58"><a href="#cb17-58" aria-hidden="true" tabindex="-1"></a>Year<span class="ot">&lt;-</span><span class="fu">seq</span>(<span class="dv">1960</span>, <span class="dv">2020</span>, <span class="dv">1</span>,)</span>
<span id="cb17-59"><a href="#cb17-59" aria-hidden="true" tabindex="-1"></a>Full_years<span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Year)</span>
<span id="cb17-60"><a href="#cb17-60" aria-hidden="true" tabindex="-1"></a>Malawi_agg_year<span class="sc">$</span>Year <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(Malawi_agg_year<span class="sc">$</span>Year)</span>
<span id="cb17-61"><a href="#cb17-61" aria-hidden="true" tabindex="-1"></a>Malawi_agg_flood<span class="sc">$</span>Year <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(Malawi_agg_flood<span class="sc">$</span>Year)</span>
<span id="cb17-62"><a href="#cb17-62" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-63"><a href="#cb17-63" aria-hidden="true" tabindex="-1"></a><span class="co"># merge</span></span>
<span id="cb17-64"><a href="#cb17-64" aria-hidden="true" tabindex="-1"></a>Malawi_agg_year_full<span class="ot">&lt;-</span> Full_years <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(Malawi_agg_year,<span class="at">by=</span><span class="fu">c</span>(<span class="st">&quot;Year&quot;</span>))</span>
<span id="cb17-65"><a href="#cb17-65" aria-hidden="true" tabindex="-1"></a>Malawi_agg_flood_full<span class="ot">&lt;-</span> Full_years <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(Malawi_agg_flood,<span class="at">by=</span><span class="fu">c</span>(<span class="st">&quot;Year&quot;</span>))</span>
<span id="cb17-66"><a href="#cb17-66" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-67"><a href="#cb17-67" aria-hidden="true" tabindex="-1"></a><span class="co"># fill in NA as 0</span></span>
<span id="cb17-68"><a href="#cb17-68" aria-hidden="true" tabindex="-1"></a>Malawi_agg_year_full <span class="ot">&lt;-</span> Malawi_agg_year_full <span class="sc">%&gt;%</span></span>
<span id="cb17-69"><a href="#cb17-69" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">total_full =</span> <span class="fu">if_else</span>(<span class="fu">is.na</span>(total), <span class="dv">0</span>, total))</span>
<span id="cb17-70"><a href="#cb17-70" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-71"><a href="#cb17-71" aria-hidden="true" tabindex="-1"></a>Malawi_agg_flood_full <span class="ot">&lt;-</span> Malawi_agg_flood_full <span class="sc">%&gt;%</span></span>
<span id="cb17-72"><a href="#cb17-72" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">flood =</span> <span class="fu">if_else</span>(<span class="fu">is.na</span>(total), <span class="dv">0</span>, total))</span>
<span id="cb17-73"><a href="#cb17-73" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-74"><a href="#cb17-74" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-75"><a href="#cb17-75" aria-hidden="true" tabindex="-1"></a>Malawi_final<span class="ot">&lt;-</span> Malawi_agg_year_full <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(Malawi_agg_flood_full,<span class="at">by=</span><span class="fu">c</span>(<span class="st">&quot;Year&quot;</span>))</span>
<span id="cb17-76"><a href="#cb17-76" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-77"><a href="#cb17-77" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-78"><a href="#cb17-78" aria-hidden="true" tabindex="-1"></a><span class="co"># create three year moving averages, otherwise not so informative </span></span>
<span id="cb17-79"><a href="#cb17-79" aria-hidden="true" tabindex="-1"></a>Malawi_final <span class="ot">&lt;-</span> Malawi_final <span class="sc">%&gt;%</span></span>
<span id="cb17-80"><a href="#cb17-80" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Year, <span class="at">srate =</span> total_full, <span class="at">srate2 =</span> flood) <span class="sc">%&gt;%</span></span>
<span id="cb17-81"><a href="#cb17-81" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb17-82"><a href="#cb17-82" aria-hidden="true" tabindex="-1"></a>    <span class="at">All =</span> zoo<span class="sc">::</span><span class="fu">rollmean</span>(srate, <span class="at">k =</span> <span class="dv">5</span>, <span class="at">fill =</span> <span class="cn">NA</span>, <span class="at">align =</span> <span class="st">&quot;center&quot;</span>),</span>
<span id="cb17-83"><a href="#cb17-83" aria-hidden="true" tabindex="-1"></a>    <span class="at">Flood =</span> zoo<span class="sc">::</span><span class="fu">rollmean</span>(srate2, <span class="at">k =</span> <span class="dv">5</span>, <span class="at">fill =</span> <span class="cn">NA</span>, <span class="at">align =</span> <span class="st">&quot;center&quot;</span>)</span>
<span id="cb17-84"><a href="#cb17-84" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb17-85"><a href="#cb17-85" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-86"><a href="#cb17-86" aria-hidden="true" tabindex="-1"></a><span class="co"># melt </span></span>
<span id="cb17-87"><a href="#cb17-87" aria-hidden="true" tabindex="-1"></a>Malawi_final<span class="ot">&lt;-</span><span class="fu">melt</span>(<span class="at">data =</span> Malawi_final, <span class="at">id.vars =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">measure.vars =</span> <span class="fu">c</span>(<span class="st">&quot;All&quot;</span>, <span class="st">&quot;Flood&quot;</span>))</span>
<span id="cb17-88"><a href="#cb17-88" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-89"><a href="#cb17-89" aria-hidden="true" tabindex="-1"></a><span class="co"># change names</span></span>
<span id="cb17-90"><a href="#cb17-90" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(Malawi_final)[<span class="fu">names</span>(Malawi_final) <span class="sc">==</span> <span class="st">&quot;variable&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Disaster&quot;</span></span>
<span id="cb17-91"><a href="#cb17-91" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-92"><a href="#cb17-92" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-93"><a href="#cb17-93" aria-hidden="true" tabindex="-1"></a><span class="co">#Plot by year</span></span>
<span id="cb17-94"><a href="#cb17-94" aria-hidden="true" tabindex="-1"></a>fig_disaster_malawi_time<span class="ot">&lt;-</span><span class="fu">ggplot</span>(<span class="at">data =</span> Malawi_final) <span class="sc">+</span></span>
<span id="cb17-95"><a href="#cb17-95" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> value, <span class="at">x =</span> Year , <span class="at">colour=</span>Disaster), </span>
<span id="cb17-96"><a href="#cb17-96" aria-hidden="true" tabindex="-1"></a>            <span class="at">alpha =</span> <span class="fl">0.6</span>,</span>
<span id="cb17-97"><a href="#cb17-97" aria-hidden="true" tabindex="-1"></a>            <span class="at">size =</span> <span class="fl">0.6</span>) <span class="sc">+</span></span>
<span id="cb17-98"><a href="#cb17-98" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis</span>() <span class="sc">+</span></span>
<span id="cb17-99"><a href="#cb17-99" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_color_manual(values = c(&quot;gray40&quot;,&quot;#09557f&quot;)) +</span></span>
<span id="cb17-100"><a href="#cb17-100" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb17-101"><a href="#cb17-101" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb17-102"><a href="#cb17-102" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.justification=</span><span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb17-103"><a href="#cb17-103" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.margin=</span><span class="fu">margin</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>),</span>
<span id="cb17-104"><a href="#cb17-104" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.box.margin=</span><span class="fu">margin</span>(<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>))<span class="sc">+</span></span>
<span id="cb17-105"><a href="#cb17-105" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">1970</span>, <span class="dv">2020</span>, <span class="at">by =</span> <span class="dv">10</span>)) <span class="sc">+</span></span>
<span id="cb17-106"><a href="#cb17-106" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>, <span class="dv">5</span>)) <span class="sc">+</span></span>
<span id="cb17-107"><a href="#cb17-107" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">y =</span><span class="st">&quot;Number of Disasters&quot;</span>)<span class="sc">+</span></span>
<span id="cb17-108"><a href="#cb17-108" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Malawi: Disasters&quot;</span>)</span>
<span id="cb17-109"><a href="#cb17-109" aria-hidden="true" tabindex="-1"></a>  <span class="co">#xlim(c(1965,2020))+ </span></span>
<span id="cb17-110"><a href="#cb17-110" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_fill_discrete(name = &quot;New Legend Title&quot;)</span></span>
<span id="cb17-111"><a href="#cb17-111" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb17-112"><a href="#cb17-112" aria-hidden="true" tabindex="-1"></a>fig_disaster_timeseries<span class="ot">&lt;-</span><span class="fu">grid.arrange</span>(fig_disaster_time,fig_disaster_malawi_time , <span class="at">ncol =</span> <span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<p><img 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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>fig_disaster_timeseries</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>TableGrob (1 x 2) &quot;arrange&quot;: 2 grobs
  z     cells    name           grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A1_disaster_timeseries.pdf&quot;</span>,<span class="at">width =</span> <span class="dv">10</span>, <span class="at">height =</span> <span class="dv">3</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>,fig_disaster_timeseries )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="figure-a2-number-of-natural-disasters-by-country-1970-and-2020-and-preparedness-by-country-2007-2011." class="level2">
<h2 class="anchored" data-anchor-id="figure-a2-number-of-natural-disasters-by-country-1970-and-2020-and-preparedness-by-country-2007-2011.">Figure A2: Number of Natural Disasters by Country (1970 and 2020) and Preparedness by Country (2007-2011).</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Read GeoJSON file</span></span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>africa_shp <span class="ot">&lt;-</span> <span class="fu">st_read</span>(<span class="st">&quot;1_input/custom.geo.json&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>Reading layer `custom.geo&#39; from data source 
  `/Users/felixhartmann/Dropbox (Personal)/Personal Folders/projects/1_active/malawi_floods/Replication/1_public/1_input/custom.geo.json&#39; 
  using driver `GeoJSON&#39;
Simple feature collection with 51 features and 64 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -17.62504 ymin: -34.81917 xmax: 51.13387 ymax: 37.34999
Geodetic CRS:  WGS 84</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Extract iso_a3 values from africa_shp</span></span>
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>iso_a3_values <span class="ot">&lt;-</span> africa_shp <span class="sc">%&gt;%</span> </span>
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">st_drop_geometry</span>() <span class="sc">%&gt;%</span>  <span class="co"># Remove geometry to just keep the data</span></span>
<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(iso_a3)  <span class="co"># Extract iso_a3 column</span></span>
<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a><span class="co"># path in downloaded data: ND_Gain/resources/indicators/id_infr_06/raw0.csv</span></span>
<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a>africa_data <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="at">file =</span> <span class="st">&#39;1_input/ND_Gain_id_infr_06_raw0.csv&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">mean_col =</span> <span class="fu">rowMeans</span>(<span class="fu">select</span>(., X2007, X2009, X2011), <span class="at">na.rm =</span> <span class="cn">TRUE</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb23-10"><a href="#cb23-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(ISO3, Name, X2007, X2009, X2011, mean_col)<span class="sc">%&gt;%</span> </span>
<span id="cb23-11"><a href="#cb23-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(ISO3 <span class="sc">%in%</span> iso_a3_values)</span>
<span id="cb23-12"><a href="#cb23-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-13"><a href="#cb23-13" aria-hidden="true" tabindex="-1"></a><span class="co"># calculate points at which to plot labels</span></span>
<span id="cb23-14"><a href="#cb23-14" aria-hidden="true" tabindex="-1"></a>centroids <span class="ot">&lt;-</span> africa_shp <span class="sc">%&gt;%</span> </span>
<span id="cb23-15"><a href="#cb23-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">st_centroid</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb23-16"><a href="#cb23-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bind_cols</span>(<span class="fu">as_tibble</span>(<span class="fu">st_coordinates</span>(.)))    <span class="co"># unpack points to lat/lon columns</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Warning: st_centroid assumes attributes are constant over geometries</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>centroids <span class="ot">&lt;-</span> centroids <span class="sc">%&gt;%</span></span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">country.flag =</span> <span class="fu">ifelse</span>(iso_a3 <span class="sc">==</span> <span class="st">&quot;MWI&quot;</span>, <span class="st">&quot;MW&quot;</span>, <span class="cn">NA</span>)) <span class="co"># &quot;MW&quot; for Malawi</span></span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>spatial_preparedness_africa<span class="ot">&lt;-</span>africa_data <span class="sc">%&gt;%</span> </span>
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(africa_shp, ., <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&#39;iso_a3&#39;</span> <span class="ot">=</span> <span class="st">&#39;ISO3&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb25-7"><a href="#cb25-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_sf</span>(<span class="fu">aes</span>(<span class="at">fill =</span> mean_col)) <span class="sc">+</span> </span>
<span id="cb25-8"><a href="#cb25-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis</span>( <span class="at">name=</span><span class="st">&quot;Disaster Preparedness&quot;</span> ) <span class="sc">+</span></span>
<span id="cb25-9"><a href="#cb25-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="fu">aes</span>(X, Y, <span class="at">label =</span> country.flag), <span class="at">data =</span> centroids, <span class="at">size =</span> <span class="dv">4</span>, <span class="at">color =</span> <span class="st">&quot;red&quot;</span>, <span class="at">na.rm =</span> <span class="cn">TRUE</span>) <span class="sc">+</span></span>
<span id="cb25-10"><a href="#cb25-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb25-11"><a href="#cb25-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Latitude&quot;</span>) <span class="sc">+</span> </span>
<span id="cb25-12"><a href="#cb25-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Longitude&quot;</span>)<span class="sc">+</span></span>
<span id="cb25-13"><a href="#cb25-13" aria-hidden="true" tabindex="-1"></a>  <span class="co">#ggtitle(&quot;Africa&quot;)+</span></span>
<span id="cb25-14"><a href="#cb25-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb25-15"><a href="#cb25-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-16"><a href="#cb25-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-17"><a href="#cb25-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-18"><a href="#cb25-18" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb25-19"><a href="#cb25-19" aria-hidden="true" tabindex="-1"></a><span class="co"># Total Number of Disasters by country</span></span>
<span id="cb25-20"><a href="#cb25-20" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb25-21"><a href="#cb25-21" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-22"><a href="#cb25-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-23"><a href="#cb25-23" aria-hidden="true" tabindex="-1"></a>agg_country<span class="ot">&lt;-</span>emdat <span class="sc">%&gt;%</span> </span>
<span id="cb25-24"><a href="#cb25-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(ISO) <span class="sc">%&gt;%</span> </span>
<span id="cb25-25"><a href="#cb25-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(total), <span class="fu">list</span>(<span class="sc">~</span><span class="fu">sum</span>(.)))</span>
<span id="cb25-26"><a href="#cb25-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-27"><a href="#cb25-27" aria-hidden="true" tabindex="-1"></a>spatial_conflict_africa<span class="ot">&lt;-</span>agg_country <span class="sc">%&gt;%</span> </span>
<span id="cb25-28"><a href="#cb25-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(africa_shp, ., <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&#39;iso_a3&#39;</span> <span class="ot">=</span> <span class="st">&#39;ISO&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb25-29"><a href="#cb25-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb25-30"><a href="#cb25-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_sf</span>(<span class="fu">aes</span>(<span class="at">fill =</span> total)) <span class="sc">+</span> </span>
<span id="cb25-31"><a href="#cb25-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis</span>(<span class="at">name=</span><span class="st">&quot;Total Disasters&quot;</span> ) <span class="sc">+</span></span>
<span id="cb25-32"><a href="#cb25-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="fu">aes</span>(X, Y, <span class="at">label =</span> country.flag), <span class="at">data =</span> centroids, <span class="at">size =</span> <span class="dv">4</span>, <span class="at">color =</span> <span class="st">&#39;red&#39;</span>)<span class="sc">+</span></span>
<span id="cb25-33"><a href="#cb25-33" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb25-34"><a href="#cb25-34" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Latitude&quot;</span>) <span class="sc">+</span> </span>
<span id="cb25-35"><a href="#cb25-35" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Longitude&quot;</span>)<span class="sc">+</span></span>
<span id="cb25-36"><a href="#cb25-36" aria-hidden="true" tabindex="-1"></a>  <span class="co">#ggtitle(&quot;Africa: Total Number of Natural Disasters&quot;)+</span></span>
<span id="cb25-37"><a href="#cb25-37" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb25-38"><a href="#cb25-38" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-39"><a href="#cb25-39" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-40"><a href="#cb25-40" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-41"><a href="#cb25-41" aria-hidden="true" tabindex="-1"></a>fig_A2_spatial<span class="ot">&lt;-</span><span class="fu">grid.arrange</span>(spatial_conflict_africa,                             </span>
<span id="cb25-42"><a href="#cb25-42" aria-hidden="true" tabindex="-1"></a>                    spatial_preparedness_africa,</span>
<span id="cb25-43"><a href="#cb25-43" aria-hidden="true" tabindex="-1"></a>                    <span class="at">nrow =</span> <span class="dv">1</span>,<span class="at">ncol=</span><span class="dv">2</span>)        </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Warning: Removed 50 rows containing missing values or values outside the scale range
(`geom_text()`).</code></pre>
</div>
<div class="cell-output-display">
<p><img 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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A2_spatial.pdf&quot;</span>,<span class="at">width =</span> <span class="dv">10</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>,fig_A2_spatial )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="figure-a3-malawi-disaster-spending-and-perceived-responsibilities" class="level2">
<h2 class="anchored" data-anchor-id="figure-a3-malawi-disaster-spending-and-perceived-responsibilities">Figure A3: Malawi Disaster Spending and Perceived Responsibilities</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Relief Aid </span></span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Humanitarian Responses: https://www.usaid.gov/sites/default/files/documents/1860/Malawi_National_Resilience_Strategy.pdf</span></span>
<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Page: 10</span></span>
<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a>Relief_million <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">2.5</span>, </span>
<span id="cb28-10"><a href="#cb28-10" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">5</span>, </span>
<span id="cb28-11"><a href="#cb28-11" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">7</span>,</span>
<span id="cb28-12"><a href="#cb28-12" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">25</span>,</span>
<span id="cb28-13"><a href="#cb28-13" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">20</span>,</span>
<span id="cb28-14"><a href="#cb28-14" aria-hidden="true" tabindex="-1"></a>                    <span class="fl">27.5</span>,</span>
<span id="cb28-15"><a href="#cb28-15" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">55</span>,</span>
<span id="cb28-16"><a href="#cb28-16" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">40</span>,</span>
<span id="cb28-17"><a href="#cb28-17" aria-hidden="true" tabindex="-1"></a>                    <span class="fl">57.5</span>,</span>
<span id="cb28-18"><a href="#cb28-18" aria-hidden="true" tabindex="-1"></a>                    <span class="fl">57.5</span>,</span>
<span id="cb28-19"><a href="#cb28-19" aria-hidden="true" tabindex="-1"></a>                    <span class="fl">57.5</span>,</span>
<span id="cb28-20"><a href="#cb28-20" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">62</span>,</span>
<span id="cb28-21"><a href="#cb28-21" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">45</span>,</span>
<span id="cb28-22"><a href="#cb28-22" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">30</span>,</span>
<span id="cb28-23"><a href="#cb28-23" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">80</span>,</span>
<span id="cb28-24"><a href="#cb28-24" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">100</span>,</span>
<span id="cb28-25"><a href="#cb28-25" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">350</span>)</span>
<span id="cb28-26"><a href="#cb28-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-27"><a href="#cb28-27" aria-hidden="true" tabindex="-1"></a>Year<span class="ot">&lt;-</span><span class="fu">seq</span>(<span class="at">from =</span> <span class="dv">2000</span>, <span class="at">to =</span> <span class="dv">2016</span>, <span class="at">by=</span><span class="dv">1</span>)</span>
<span id="cb28-28"><a href="#cb28-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-29"><a href="#cb28-29" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-30"><a href="#cb28-30" aria-hidden="true" tabindex="-1"></a>response <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(<span class="fu">cbind</span>(Year,Relief_million))</span>
<span id="cb28-31"><a href="#cb28-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-32"><a href="#cb28-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-33"><a href="#cb28-33" aria-hidden="true" tabindex="-1"></a><span class="do">##########################################</span></span>
<span id="cb28-34"><a href="#cb28-34" aria-hidden="true" tabindex="-1"></a><span class="co"># Adaption (Prevention id)</span></span>
<span id="cb28-35"><a href="#cb28-35" aria-hidden="true" tabindex="-1"></a><span class="do">##########################################</span></span>
<span id="cb28-36"><a href="#cb28-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-37"><a href="#cb28-37" aria-hidden="true" tabindex="-1"></a>Malawi_Geocoded_Climate_Dataset <span class="ot">&lt;-</span> <span class="fu">read_excel</span>(<span class="st">&quot;1_input/Malawi Geocoded Climate Dataset.xlsx&quot;</span>, </span>
<span id="cb28-38"><a href="#cb28-38" aria-hidden="true" tabindex="-1"></a>                                              <span class="at">sheet =</span> <span class="st">&quot;All Projects&quot;</span>)</span>
<span id="cb28-39"><a href="#cb28-39" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-40"><a href="#cb28-40" aria-hidden="true" tabindex="-1"></a>climate_projects <span class="ot">&lt;-</span> <span class="fu">subset</span>(Malawi_Geocoded_Climate_Dataset, <span class="st">`</span><span class="at">Climate Spectrum Assignment</span><span class="st">`</span> <span class="sc">==</span><span class="st">&quot;Climate Oriented&quot;</span>)</span>
<span id="cb28-41"><a href="#cb28-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-42"><a href="#cb28-42" aria-hidden="true" tabindex="-1"></a><span class="co"># keep if amount specified</span></span>
<span id="cb28-43"><a href="#cb28-43" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects[<span class="sc">!</span><span class="fu">is.na</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Cumulative Disbursement</span><span class="st">`</span>), ]</span>
<span id="cb28-44"><a href="#cb28-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-45"><a href="#cb28-45" aria-hidden="true" tabindex="-1"></a><span class="co"># if date of signment is &quot;NA&quot; or &quot;0&quot;, use the completion date if possible</span></span>
<span id="cb28-46"><a href="#cb28-46" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span> <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>), climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Planned Completion</span><span class="st">`</span>, climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>)</span>
<span id="cb28-47"><a href="#cb28-47" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span> <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span><span class="sc">==</span><span class="dv">0</span>, climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Planned Completion</span><span class="st">`</span>, climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>)</span>
<span id="cb28-48"><a href="#cb28-48" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-49"><a href="#cb28-49" aria-hidden="true" tabindex="-1"></a><span class="co"># keep if year of agreement is identified </span></span>
<span id="cb28-50"><a href="#cb28-50" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects[<span class="sc">!</span><span class="fu">is.na</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>), ]</span>
<span id="cb28-51"><a href="#cb28-51" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-52"><a href="#cb28-52" aria-hidden="true" tabindex="-1"></a><span class="co"># drop if year is marked as &quot;0&quot; (coding error?)</span></span>
<span id="cb28-53"><a href="#cb28-53" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span><span class="fu">subset</span>(climate_projects,<span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span><span class="sc">!=</span><span class="dv">0</span>)</span>
<span id="cb28-54"><a href="#cb28-54" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-55"><a href="#cb28-55" aria-hidden="true" tabindex="-1"></a><span class="co"># amount in millins US $</span></span>
<span id="cb28-56"><a href="#cb28-56" aria-hidden="true" tabindex="-1"></a>climate_projects[<span class="fu">c</span>(<span class="st">&quot;Cumulative Disbursement&quot;</span>)] <span class="ot">&lt;-</span> climate_projects[<span class="fu">c</span>(<span class="st">&quot;Cumulative Disbursement&quot;</span>)]<span class="sc">/</span><span class="fl">1e6</span></span>
<span id="cb28-57"><a href="#cb28-57" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-58"><a href="#cb28-58" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-59"><a href="#cb28-59" aria-hidden="true" tabindex="-1"></a><span class="co"># standardize date</span></span>
<span id="cb28-60"><a href="#cb28-60" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects <span class="sc">%&gt;%</span> </span>
<span id="cb28-61"><a href="#cb28-61" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">as.character</span>(<span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-62"><a href="#cb28-62" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/03/2006&#39;</span>, <span class="st">&#39;2006&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-63"><a href="#cb28-63" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/05/2009&#39;</span>, <span class="st">&#39;2009&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-64"><a href="#cb28-64" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/1999&#39;</span>, <span class="st">&#39;1999&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-65"><a href="#cb28-65" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2001&#39;</span>, <span class="st">&#39;2001&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-66"><a href="#cb28-66" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2002&#39;</span>, <span class="st">&#39;2002&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-67"><a href="#cb28-67" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2003&#39;</span>, <span class="st">&#39;2003&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-68"><a href="#cb28-68" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2006&#39;</span>, <span class="st">&#39;2006&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-69"><a href="#cb28-69" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-70"><a href="#cb28-70" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2010&#39;</span>, <span class="st">&#39;2010&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-71"><a href="#cb28-71" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/08/2005&#39;</span>, <span class="st">&#39;2005&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-72"><a href="#cb28-72" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/09/2007&#39;</span>, <span class="st">&#39;2007&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-73"><a href="#cb28-73" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/09/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-74"><a href="#cb28-74" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/10/2004&#39;</span>, <span class="st">&#39;2004&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-75"><a href="#cb28-75" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/11/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-76"><a href="#cb28-76" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/11/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-77"><a href="#cb28-77" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/12/2007&#39;</span>, <span class="st">&#39;2007&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-78"><a href="#cb28-78" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/12/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-79"><a href="#cb28-79" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;02/01/2001&#39;</span>, <span class="st">&#39;2001&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-80"><a href="#cb28-80" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;02/01/2003&#39;</span>, <span class="st">&#39;2003&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-81"><a href="#cb28-81" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;11/04/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-82"><a href="#cb28-82" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;12/09/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-83"><a href="#cb28-83" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;12/09/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-84"><a href="#cb28-84" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;15/12/2006&#39;</span>, <span class="st">&#39;2006&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-85"><a href="#cb28-85" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;23/01/2007&#39;</span>, <span class="st">&#39;2007&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-86"><a href="#cb28-86" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;19/11/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>))<span class="sc">%&gt;%</span> </span>
<span id="cb28-87"><a href="#cb28-87" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;05/05/2010&#39;</span>, <span class="st">&#39;2010&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-88"><a href="#cb28-88" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;31/12/2009&#39;</span>, <span class="st">&#39;2009&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-89"><a href="#cb28-89" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;31/03/2011&#39;</span>, <span class="st">&#39;2011&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-90"><a href="#cb28-90" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;31/03/2010&#39;</span>, <span class="st">&#39;2010&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-91"><a href="#cb28-91" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;30/08/2013&#39;</span>, <span class="st">&#39;2013&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb28-92"><a href="#cb28-92" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;30/06/2009&#39;</span>, <span class="st">&#39;2009&#39;</span>))</span>
<span id="cb28-93"><a href="#cb28-93" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-94"><a href="#cb28-94" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-95"><a href="#cb28-95" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-96"><a href="#cb28-96" aria-hidden="true" tabindex="-1"></a><span class="co"># only keep unique</span></span>
<span id="cb28-97"><a href="#cb28-97" aria-hidden="true" tabindex="-1"></a><span class="co"># some monyy targets to many places</span></span>
<span id="cb28-98"><a href="#cb28-98" aria-hidden="true" tabindex="-1"></a><span class="co"># Remove duplicates based on Sepal.Width columns</span></span>
<span id="cb28-99"><a href="#cb28-99" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects[<span class="sc">!</span><span class="fu">duplicated</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Cumulative Disbursement</span><span class="st">`</span>), ]</span>
<span id="cb28-100"><a href="#cb28-100" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-101"><a href="#cb28-101" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-102"><a href="#cb28-102" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects<span class="sc">%&gt;%</span> </span>
<span id="cb28-103"><a href="#cb28-103" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(<span class="st">`</span><span class="at">Year</span><span class="st">`</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb28-104"><a href="#cb28-104" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(<span class="st">`</span><span class="at">Cumulative Disbursement</span><span class="st">`</span> ), <span class="fu">funs</span>(sum))</span>
<span id="cb28-105"><a href="#cb28-105" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-106"><a href="#cb28-106" aria-hidden="true" tabindex="-1"></a><span class="co"># Merge Prevention and Relief Aid Spending </span></span>
<span id="cb28-107"><a href="#cb28-107" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-108"><a href="#cb28-108" aria-hidden="true" tabindex="-1"></a><span class="co"># INNER JOIN: returns rows when there is a match in both tables.</span></span>
<span id="cb28-109"><a href="#cb28-109" aria-hidden="true" tabindex="-1"></a>prevention_response<span class="ot">&lt;-</span><span class="fu">merge</span>(climate_projects, response, <span class="at">all.y=</span><span class="cn">TRUE</span>)</span>
<span id="cb28-110"><a href="#cb28-110" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-111"><a href="#cb28-111" aria-hidden="true" tabindex="-1"></a><span class="co"># rename</span></span>
<span id="cb28-112"><a href="#cb28-112" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(prevention_response)[<span class="fu">names</span>(prevention_response) <span class="sc">==</span> <span class="st">&quot;Cumulative Disbursement&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Prevention&quot;</span></span>
<span id="cb28-113"><a href="#cb28-113" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(prevention_response)[<span class="fu">names</span>(prevention_response) <span class="sc">==</span> <span class="st">&quot;Relief_million&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Relief&quot;</span></span>
<span id="cb28-114"><a href="#cb28-114" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-115"><a href="#cb28-115" aria-hidden="true" tabindex="-1"></a><span class="co"># melt </span></span>
<span id="cb28-116"><a href="#cb28-116" aria-hidden="true" tabindex="-1"></a>prevention_response_final<span class="ot">&lt;-</span><span class="fu">melt</span>(<span class="at">data =</span> prevention_response, <span class="at">id.vars =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">measure.vars =</span> <span class="fu">c</span>(<span class="st">&quot;Prevention&quot;</span>, <span class="st">&quot;Relief&quot;</span>))</span>
<span id="cb28-117"><a href="#cb28-117" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-118"><a href="#cb28-118" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-119"><a href="#cb28-119" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb28-120"><a href="#cb28-120" aria-hidden="true" tabindex="-1"></a>fig_spending<span class="ot">&lt;-</span><span class="fu">ggplot</span>() <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">y =</span> value, <span class="at">x =</span><span class="fu">as.numeric</span>(Year), <span class="at">fill =</span> variable),</span>
<span id="cb28-121"><a href="#cb28-121" aria-hidden="true" tabindex="-1"></a>                        <span class="at">data =</span> prevention_response_final,</span>
<span id="cb28-122"><a href="#cb28-122" aria-hidden="true" tabindex="-1"></a>                        <span class="at">stat=</span><span class="st">&quot;identity&quot;</span>)<span class="sc">+</span> </span>
<span id="cb28-123"><a href="#cb28-123" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">16</span>) <span class="sc">+</span></span>
<span id="cb28-124"><a href="#cb28-124" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_grey</span>()<span class="sc">+</span></span>
<span id="cb28-125"><a href="#cb28-125" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)<span class="sc">+</span></span>
<span id="cb28-126"><a href="#cb28-126" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">y =</span><span class="st">&quot;Total Amount in Million US$&quot;</span>)<span class="sc">+</span></span>
<span id="cb28-127"><a href="#cb28-127" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Prevention &amp; Relief Spending&quot;</span>)<span class="sc">+</span></span>
<span id="cb28-128"><a href="#cb28-128" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">title=</span><span class="st">&quot; &quot;</span>))</span>
<span id="cb28-129"><a href="#cb28-129" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-130"><a href="#cb28-130" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-131"><a href="#cb28-131" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-132"><a href="#cb28-132" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-133"><a href="#cb28-133" aria-hidden="true" tabindex="-1"></a><span class="co"># Responsabilities</span></span>
<span id="cb28-134"><a href="#cb28-134" aria-hidden="true" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;1_input/covariates.rds&quot;</span>)</span>
<span id="cb28-135"><a href="#cb28-135" aria-hidden="true" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> covariates <span class="sc">%&gt;%</span></span>
<span id="cb28-136"><a href="#cb28-136" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(</span>
<span id="cb28-137"><a href="#cb28-137" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_ta&quot;</span> <span class="ot">=</span> T_q38_1,</span>
<span id="cb28-138"><a href="#cb28-138" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_village_head&quot;</span> <span class="ot">=</span> T_q38_2,</span>
<span id="cb28-139"><a href="#cb28-139" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_relig_leader&quot;</span> <span class="ot">=</span> T_q38_3,</span>
<span id="cb28-140"><a href="#cb28-140" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_mp&quot;</span> <span class="ot">=</span> T_q38_4,</span>
<span id="cb28-141"><a href="#cb28-141" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_local_council&quot;</span> <span class="ot">=</span> T_q38_5,</span>
<span id="cb28-142"><a href="#cb28-142" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_district_commissioner&quot;</span> <span class="ot">=</span> T_q38_7,</span>
<span id="cb28-143"><a href="#cb28-143" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_ngo_io&quot;</span> <span class="ot">=</span> T_q38_8,</span>
<span id="cb28-144"><a href="#cb28-144" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_dodma&quot;</span> <span class="ot">=</span> T_q38_9,</span>
<span id="cb28-145"><a href="#cb28-145" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_ta&quot;</span> <span class="ot">=</span> T_q37_1,</span>
<span id="cb28-146"><a href="#cb28-146" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_village_head&quot;</span> <span class="ot">=</span> T_q37_2,</span>
<span id="cb28-147"><a href="#cb28-147" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_relig_leader&quot;</span> <span class="ot">=</span> T_q37_3,</span>
<span id="cb28-148"><a href="#cb28-148" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_mp&quot;</span> <span class="ot">=</span> T_q37_4,</span>
<span id="cb28-149"><a href="#cb28-149" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_local_council&quot;</span> <span class="ot">=</span> T_q37_5,</span>
<span id="cb28-150"><a href="#cb28-150" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_district_commissioner&quot;</span> <span class="ot">=</span> T_q37_7,</span>
<span id="cb28-151"><a href="#cb28-151" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_ngo_io&quot;</span> <span class="ot">=</span> T_q37_8,</span>
<span id="cb28-152"><a href="#cb28-152" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_dodma&quot;</span> <span class="ot">=</span> T_q37_9</span>
<span id="cb28-153"><a href="#cb28-153" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb28-154"><a href="#cb28-154" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-155"><a href="#cb28-155" aria-hidden="true" tabindex="-1"></a><span class="co"># List of the renamed variables for preparation and relief</span></span>
<span id="cb28-156"><a href="#cb28-156" aria-hidden="true" tabindex="-1"></a>prep_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;resp_prepar_ta&quot;</span>, <span class="st">&quot;resp_prepar_village_head&quot;</span>, <span class="st">&quot;resp_prepar_relig_leader&quot;</span>, </span>
<span id="cb28-157"><a href="#cb28-157" aria-hidden="true" tabindex="-1"></a>               <span class="st">&quot;resp_prepar_mp&quot;</span>, <span class="st">&quot;resp_prepar_local_council&quot;</span>, <span class="st">&quot;resp_prepar_district_commissioner&quot;</span>, </span>
<span id="cb28-158"><a href="#cb28-158" aria-hidden="true" tabindex="-1"></a>               <span class="st">&quot;resp_prepar_ngo_io&quot;</span>, <span class="st">&quot;resp_prepar_dodma&quot;</span>)</span>
<span id="cb28-159"><a href="#cb28-159" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-160"><a href="#cb28-160" aria-hidden="true" tabindex="-1"></a>relief_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;resp_relief_ta&quot;</span>, <span class="st">&quot;resp_relief_village_head&quot;</span>, <span class="st">&quot;resp_relief_relig_leader&quot;</span>, </span>
<span id="cb28-161"><a href="#cb28-161" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&quot;resp_relief_mp&quot;</span>, <span class="st">&quot;resp_relief_local_council&quot;</span>, <span class="st">&quot;resp_relief_district_commissioner&quot;</span>, </span>
<span id="cb28-162"><a href="#cb28-162" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&quot;resp_relief_ngo_io&quot;</span>, <span class="st">&quot;resp_relief_dodma&quot;</span>)</span>
<span id="cb28-163"><a href="#cb28-163" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-164"><a href="#cb28-164" aria-hidden="true" tabindex="-1"></a><span class="co"># Function to count the number of &quot;3&quot; responses for a given set of variables</span></span>
<span id="cb28-165"><a href="#cb28-165" aria-hidden="true" tabindex="-1"></a>count_responsibility <span class="ot">&lt;-</span> <span class="cf">function</span>(df, variables) {</span>
<span id="cb28-166"><a href="#cb28-166" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Ensure that the variables are numeric and count the &quot;3&quot; responses</span></span>
<span id="cb28-167"><a href="#cb28-167" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sapply</span>(variables, <span class="cf">function</span>(var) <span class="fu">sum</span>(<span class="fu">as.numeric</span>(df[[var]]) <span class="sc">==</span> <span class="dv">3</span>, <span class="at">na.rm =</span> <span class="cn">TRUE</span>))</span>
<span id="cb28-168"><a href="#cb28-168" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb28-169"><a href="#cb28-169" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-170"><a href="#cb28-170" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-171"><a href="#cb28-171" aria-hidden="true" tabindex="-1"></a><span class="co"># Count &quot;3&quot; responses for preparation and relief</span></span>
<span id="cb28-172"><a href="#cb28-172" aria-hidden="true" tabindex="-1"></a>Prevention <span class="ot">&lt;-</span> <span class="fu">count_responsibility</span>(covariates, prep_vars)</span>
<span id="cb28-173"><a href="#cb28-173" aria-hidden="true" tabindex="-1"></a>Relief <span class="ot">&lt;-</span> <span class="fu">count_responsibility</span>(covariates, relief_vars)</span>
<span id="cb28-174"><a href="#cb28-174" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-175"><a href="#cb28-175" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a dataframe for plotting</span></span>
<span id="cb28-176"><a href="#cb28-176" aria-hidden="true" tabindex="-1"></a>Office <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;TA&quot;</span>, <span class="st">&quot;Village </span><span class="sc">\n</span><span class="st"> Head&quot;</span>, <span class="st">&quot;Relig. </span><span class="sc">\n</span><span class="st"> Leader&quot;</span>, <span class="st">&quot;MP&quot;</span>, <span class="st">&quot;Council&quot;</span>, </span>
<span id="cb28-177"><a href="#cb28-177" aria-hidden="true" tabindex="-1"></a>            <span class="st">&quot;District&quot;</span>, <span class="st">&quot;IO </span><span class="sc">\n</span><span class="st"> NGO&quot;</span>, <span class="st">&quot;DoDMA&quot;</span>)</span>
<span id="cb28-178"><a href="#cb28-178" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the counts into a single dataframe</span></span>
<span id="cb28-179"><a href="#cb28-179" aria-hidden="true" tabindex="-1"></a>response <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(Office, Prevention, Relief)</span>
<span id="cb28-180"><a href="#cb28-180" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-181"><a href="#cb28-181" aria-hidden="true" tabindex="-1"></a><span class="co"># Reshape the data for plotting (long format)</span></span>
<span id="cb28-182"><a href="#cb28-182" aria-hidden="true" tabindex="-1"></a>response_long <span class="ot">&lt;-</span> response <span class="sc">%&gt;%</span></span>
<span id="cb28-183"><a href="#cb28-183" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="at">cols =</span> <span class="fu">c</span>(<span class="st">&quot;Prevention&quot;</span>, <span class="st">&quot;Relief&quot;</span>), <span class="at">names_to =</span> <span class="st">&quot;Category&quot;</span>, <span class="at">values_to =</span> <span class="st">&quot;Count&quot;</span>)</span>
<span id="cb28-184"><a href="#cb28-184" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-185"><a href="#cb28-185" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the data</span></span>
<span id="cb28-186"><a href="#cb28-186" aria-hidden="true" tabindex="-1"></a>responsability<span class="ot">&lt;-</span><span class="fu">ggplot</span>(response_long, <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">reorder</span>(Office, <span class="sc">-</span>Count), <span class="at">y =</span> Count, <span class="at">fill =</span> Category)) <span class="sc">+</span></span>
<span id="cb28-187"><a href="#cb28-187" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>) <span class="sc">+</span></span>
<span id="cb28-188"><a href="#cb28-188" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_grey</span>()<span class="sc">+</span></span>
<span id="cb28-189"><a href="#cb28-189" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">16</span>) <span class="sc">+</span></span>
<span id="cb28-190"><a href="#cb28-190" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)<span class="sc">+</span></span>
<span id="cb28-191"><a href="#cb28-191" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">y =</span><span class="st">&quot;Total Number of Responses&quot;</span>)<span class="sc">+</span></span>
<span id="cb28-192"><a href="#cb28-192" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Prevention &amp; Relief Responsibilities Malawi&quot;</span>)<span class="sc">+</span></span>
<span id="cb28-193"><a href="#cb28-193" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">title=</span><span class="st">&quot; &quot;</span>))</span>
<span id="cb28-194"><a href="#cb28-194" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-195"><a href="#cb28-195" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-196"><a href="#cb28-196" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-197"><a href="#cb28-197" aria-hidden="true" tabindex="-1"></a>fig_A3<span class="ot">&lt;-</span><span class="fu">ggarrange</span>(</span>
<span id="cb28-198"><a href="#cb28-198" aria-hidden="true" tabindex="-1"></a>  fig_spending, responsability,</span>
<span id="cb28-199"><a href="#cb28-199" aria-hidden="true" tabindex="-1"></a>  <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend =</span> <span class="st">&quot;bottom&quot;</span></span>
<span id="cb28-200"><a href="#cb28-200" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb28-201"><a href="#cb28-201" aria-hidden="true" tabindex="-1"></a>fig_A3</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<p><img 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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A3_malawi.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="figure-a5-help-received-after-the-2015-flood-left-and-satisfaction-with-response-right" class="level2">
<h2 class="anchored" data-anchor-id="figure-a5-help-received-after-the-2015-flood-left-and-satisfaction-with-response-right">Figure A5: Help received after the 2015 flood (left) and satisfaction with response (right)</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>covariates<span class="ot">&lt;-</span><span class="fu">readRDS</span>(<span class="at">file =</span> <span class="st">&quot;1_input/covariates.rds&quot;</span>)</span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>covariates<span class="ot">&lt;-</span>covariates <span class="sc">%&gt;%</span> </span>
<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">drop_na</span>(satisfied_personal_help) <span class="sc">%&gt;%</span></span>
<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a>     dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">personal_help =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a>              personal_help <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No&quot;</span>,</span>
<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a>              personal_help <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Yes&quot;</span></span>
<span id="cb30-10"><a href="#cb30-10" aria-hidden="true" tabindex="-1"></a>              ),</span>
<span id="cb30-11"><a href="#cb30-11" aria-hidden="true" tabindex="-1"></a>       <span class="at">community_help =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-12"><a href="#cb30-12" aria-hidden="true" tabindex="-1"></a>              community_help <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No&quot;</span>,</span>
<span id="cb30-13"><a href="#cb30-13" aria-hidden="true" tabindex="-1"></a>              community_help <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Yes&quot;</span></span>
<span id="cb30-14"><a href="#cb30-14" aria-hidden="true" tabindex="-1"></a>              ),</span>
<span id="cb30-15"><a href="#cb30-15" aria-hidden="true" tabindex="-1"></a>       <span class="at">satisfied_personal_help =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-16"><a href="#cb30-16" aria-hidden="true" tabindex="-1"></a>              satisfied_personal_help <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Very Satisfied&quot;</span>,</span>
<span id="cb30-17"><a href="#cb30-17" aria-hidden="true" tabindex="-1"></a>              satisfied_personal_help <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot; Somewhat Satisfied&quot;</span>,</span>
<span id="cb30-18"><a href="#cb30-18" aria-hidden="true" tabindex="-1"></a>              satisfied_personal_help <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;  Somewhat Dissatisfied&quot;</span>,</span>
<span id="cb30-19"><a href="#cb30-19" aria-hidden="true" tabindex="-1"></a>              satisfied_personal_help <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;   Very Dissatisfied&quot;</span>,</span>
<span id="cb30-20"><a href="#cb30-20" aria-hidden="true" tabindex="-1"></a>                         <span class="cn">TRUE</span> <span class="sc">~</span> satisfied_personal_help),</span>
<span id="cb30-21"><a href="#cb30-21" aria-hidden="true" tabindex="-1"></a>      <span class="at">community_help_id1 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-22"><a href="#cb30-22" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Traditional authority&quot;</span>,</span>
<span id="cb30-23"><a href="#cb30-23" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Village head&quot;</span>,</span>
<span id="cb30-24"><a href="#cb30-24" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Religious leader&quot;</span>,</span>
<span id="cb30-25"><a href="#cb30-25" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb30-26"><a href="#cb30-26" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Local council member&quot;</span>,</span>
<span id="cb30-27"><a href="#cb30-27" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;District commissioner&quot;</span>,</span>
<span id="cb30-28"><a href="#cb30-28" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb30-29"><a href="#cb30-29" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;Dodma&quot;</span>,</span>
<span id="cb30-30"><a href="#cb30-30" aria-hidden="true" tabindex="-1"></a>              <span class="cn">TRUE</span> <span class="sc">~</span> community_help_id1),</span>
<span id="cb30-31"><a href="#cb30-31" aria-hidden="true" tabindex="-1"></a>      <span class="at">community_help_id2 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-32"><a href="#cb30-32" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Traditional authority&quot;</span>,</span>
<span id="cb30-33"><a href="#cb30-33" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Village head&quot;</span>,</span>
<span id="cb30-34"><a href="#cb30-34" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Religious leader&quot;</span>,</span>
<span id="cb30-35"><a href="#cb30-35" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb30-36"><a href="#cb30-36" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Local council member&quot;</span>,</span>
<span id="cb30-37"><a href="#cb30-37" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;District commissioner&quot;</span>,</span>
<span id="cb30-38"><a href="#cb30-38" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb30-39"><a href="#cb30-39" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;Dodma&quot;</span>,</span>
<span id="cb30-40"><a href="#cb30-40" aria-hidden="true" tabindex="-1"></a>              <span class="cn">TRUE</span> <span class="sc">~</span> community_help_id2),</span>
<span id="cb30-41"><a href="#cb30-41" aria-hidden="true" tabindex="-1"></a>      <span class="at">community_help_id3 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-42"><a href="#cb30-42" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Traditional authority&quot;</span>,</span>
<span id="cb30-43"><a href="#cb30-43" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Village head&quot;</span>,</span>
<span id="cb30-44"><a href="#cb30-44" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Religious leader&quot;</span>,</span>
<span id="cb30-45"><a href="#cb30-45" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb30-46"><a href="#cb30-46" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Local council member&quot;</span>,</span>
<span id="cb30-47"><a href="#cb30-47" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;District commissioner&quot;</span>,</span>
<span id="cb30-48"><a href="#cb30-48" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb30-49"><a href="#cb30-49" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;Dodma&quot;</span>,</span>
<span id="cb30-50"><a href="#cb30-50" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> community_help_id3),</span>
<span id="cb30-51"><a href="#cb30-51" aria-hidden="true" tabindex="-1"></a>      <span class="at">personal_help_id1 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-52"><a href="#cb30-52" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Family&quot;</span>,</span>
<span id="cb30-53"><a href="#cb30-53" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Friends&quot;</span>,</span>
<span id="cb30-54"><a href="#cb30-54" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Traditional Authority&quot;</span>,</span>
<span id="cb30-55"><a href="#cb30-55" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Village Head&quot;</span>,</span>
<span id="cb30-56"><a href="#cb30-56" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Religious Leader&quot;</span>,</span>
<span id="cb30-57"><a href="#cb30-57" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb30-58"><a href="#cb30-58" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;Local Council&quot;</span>,</span>
<span id="cb30-59"><a href="#cb30-59" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;District Commissioner&quot;</span>,</span>
<span id="cb30-60"><a href="#cb30-60" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">9</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb30-61"><a href="#cb30-61" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">10</span> <span class="sc">~</span> <span class="st">&quot;Department of Disaster Management&quot;</span>,</span>
<span id="cb30-62"><a href="#cb30-62" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">11</span> <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb30-63"><a href="#cb30-63" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> personal_help_id1),</span>
<span id="cb30-64"><a href="#cb30-64" aria-hidden="true" tabindex="-1"></a>      <span class="at">personal_help_id2 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-65"><a href="#cb30-65" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Family&quot;</span>,</span>
<span id="cb30-66"><a href="#cb30-66" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Friends&quot;</span>,</span>
<span id="cb30-67"><a href="#cb30-67" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Traditional Authority&quot;</span>,</span>
<span id="cb30-68"><a href="#cb30-68" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Village Head&quot;</span>,</span>
<span id="cb30-69"><a href="#cb30-69" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Religious Leader&quot;</span>,</span>
<span id="cb30-70"><a href="#cb30-70" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb30-71"><a href="#cb30-71" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;Local Council&quot;</span>,</span>
<span id="cb30-72"><a href="#cb30-72" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;District Commissioner&quot;</span>,</span>
<span id="cb30-73"><a href="#cb30-73" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">9</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb30-74"><a href="#cb30-74" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">10</span> <span class="sc">~</span> <span class="st">&quot;Department of Disaster Management&quot;</span>,</span>
<span id="cb30-75"><a href="#cb30-75" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">11</span> <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb30-76"><a href="#cb30-76" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> personal_help_id2),</span>
<span id="cb30-77"><a href="#cb30-77" aria-hidden="true" tabindex="-1"></a>      <span class="at">personal_help_id3 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-78"><a href="#cb30-78" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Family&quot;</span>,</span>
<span id="cb30-79"><a href="#cb30-79" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Friends&quot;</span>,</span>
<span id="cb30-80"><a href="#cb30-80" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Traditional Authority&quot;</span>,</span>
<span id="cb30-81"><a href="#cb30-81" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Village Head&quot;</span>,</span>
<span id="cb30-82"><a href="#cb30-82" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Religious Leader&quot;</span>,</span>
<span id="cb30-83"><a href="#cb30-83" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb30-84"><a href="#cb30-84" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;Local Council&quot;</span>,</span>
<span id="cb30-85"><a href="#cb30-85" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;District Commissioner&quot;</span>,</span>
<span id="cb30-86"><a href="#cb30-86" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">9</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb30-87"><a href="#cb30-87" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">10</span> <span class="sc">~</span> <span class="st">&quot;Department of Disaster Management&quot;</span>,</span>
<span id="cb30-88"><a href="#cb30-88" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">11</span> <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb30-89"><a href="#cb30-89" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> personal_help_id3),</span>
<span id="cb30-90"><a href="#cb30-90" aria-hidden="true" tabindex="-1"></a>      <span class="at">personal_help_id4 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb30-91"><a href="#cb30-91" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Family&quot;</span>,</span>
<span id="cb30-92"><a href="#cb30-92" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Friends&quot;</span>,</span>
<span id="cb30-93"><a href="#cb30-93" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Traditional Authority&quot;</span>,</span>
<span id="cb30-94"><a href="#cb30-94" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Village Head&quot;</span>,</span>
<span id="cb30-95"><a href="#cb30-95" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Religious Leader&quot;</span>,</span>
<span id="cb30-96"><a href="#cb30-96" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb30-97"><a href="#cb30-97" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;Local Council&quot;</span>,</span>
<span id="cb30-98"><a href="#cb30-98" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;District Commissioner&quot;</span>,</span>
<span id="cb30-99"><a href="#cb30-99" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">9</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb30-100"><a href="#cb30-100" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">10</span> <span class="sc">~</span> <span class="st">&quot;Department of Disaster Management&quot;</span>,</span>
<span id="cb30-101"><a href="#cb30-101" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">11</span> <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb30-102"><a href="#cb30-102" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> personal_help_id4)</span>
<span id="cb30-103"><a href="#cb30-103" aria-hidden="true" tabindex="-1"></a>      ) </span>
<span id="cb30-104"><a href="#cb30-104" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb30-105"><a href="#cb30-105" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-106"><a href="#cb30-106" aria-hidden="true" tabindex="-1"></a>personal_help<span class="ot">&lt;-</span>covariates <span class="sc">%&gt;%</span></span>
<span id="cb30-107"><a href="#cb30-107" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">count</span>(personal_help) <span class="sc">%&gt;%</span></span>
<span id="cb30-108"><a href="#cb30-108" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">sum_group =</span> n) <span class="sc">%&gt;%</span></span>
<span id="cb30-109"><a href="#cb30-109" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb30-110"><a href="#cb30-110" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">x=</span>personal_help,<span class="at">y =</span> (sum_group)<span class="sc">/</span><span class="fu">sum</span>(sum_group)),<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>,<span class="at">width=</span><span class="fl">0.4</span>) <span class="sc">+</span></span>
<span id="cb30-111"><a href="#cb30-111" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">labels=</span>scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb30-112"><a href="#cb30-112" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Density&#39;</span>,<span class="at">x=</span><span class="st">&#39;After the 2015 floods, did you personally receive help?&#39;</span>)<span class="sc">+</span></span>
<span id="cb30-113"><a href="#cb30-113" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
<span id="cb30-114"><a href="#cb30-114" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-115"><a href="#cb30-115" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-116"><a href="#cb30-116" aria-hidden="true" tabindex="-1"></a>satisfied_personal_help<span class="ot">&lt;-</span>covariates <span class="sc">%&gt;%</span></span>
<span id="cb30-117"><a href="#cb30-117" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(satisfied_personal_help<span class="sc">!=</span><span class="st">&quot;.&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb30-118"><a href="#cb30-118" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">count</span>(satisfied_personal_help) <span class="sc">%&gt;%</span></span>
<span id="cb30-119"><a href="#cb30-119" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">sum_group =</span> n) <span class="sc">%&gt;%</span></span>
<span id="cb30-120"><a href="#cb30-120" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb30-121"><a href="#cb30-121" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">x=</span>satisfied_personal_help,<span class="at">y =</span> (sum_group)<span class="sc">/</span><span class="fu">sum</span>(sum_group)),<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>,<span class="at">width=</span><span class="fl">0.4</span>) <span class="sc">+</span></span>
<span id="cb30-122"><a href="#cb30-122" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">labels=</span>scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb30-123"><a href="#cb30-123" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Density&#39;</span>,<span class="at">x=</span><span class="st">&#39;How satisfied were you with the help you received?&#39;</span>)<span class="sc">+</span></span>
<span id="cb30-124"><a href="#cb30-124" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
<span id="cb30-125"><a href="#cb30-125" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-126"><a href="#cb30-126" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-127"><a href="#cb30-127" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-128"><a href="#cb30-128" aria-hidden="true" tabindex="-1"></a>help_rec <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(personal_help,satisfied_personal_help,</span>
<span id="cb30-129"><a href="#cb30-129" aria-hidden="true" tabindex="-1"></a>                    <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>)</span>
<span id="cb30-130"><a href="#cb30-130" aria-hidden="true" tabindex="-1"></a>help_rec</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A5_help_rec.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="figure-a6-actors-help-received-left-and-satisfaction-when-received-help-only-from-mp-right" class="level2">
<h2 class="anchored" data-anchor-id="figure-a6-actors-help-received-left-and-satisfaction-when-received-help-only-from-mp-right">Figure A6: Actors help received (left) and satisfaction when received help only from MP (right)</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a><span class="co"># [q31/Q_48_S] From whom did you receive help? (only personal)</span></span>
<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a>personal_help_id1<span class="ot">&lt;-</span><span class="fu">as.data.frame</span>(covariates<span class="sc">$</span>personal_help_id1)</span>
<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>personal_help_id2<span class="ot">&lt;-</span><span class="fu">as.data.frame</span>(covariates<span class="sc">$</span>personal_help_id2)</span>
<span id="cb32-8"><a href="#cb32-8" aria-hidden="true" tabindex="-1"></a>personal_help_id3<span class="ot">&lt;-</span><span class="fu">as.data.frame</span>(covariates<span class="sc">$</span>personal_help_id3)</span>
<span id="cb32-9"><a href="#cb32-9" aria-hidden="true" tabindex="-1"></a>personal_help_id4<span class="ot">&lt;-</span><span class="fu">as.data.frame</span>(covariates<span class="sc">$</span>personal_help_id4)</span>
<span id="cb32-10"><a href="#cb32-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-11"><a href="#cb32-11" aria-hidden="true" tabindex="-1"></a><span class="co"># drop all missing</span></span>
<span id="cb32-12"><a href="#cb32-12" aria-hidden="true" tabindex="-1"></a>personal_help_id1<span class="ot">&lt;-</span><span class="fu">subset</span>(personal_help_id1, personal_help_id1<span class="sc">!=</span><span class="st">&quot;.&quot;</span> <span class="sc">&amp;</span> personal_help_id1<span class="sc">!=</span><span class="st">&quot;NA&quot;</span>)</span>
<span id="cb32-13"><a href="#cb32-13" aria-hidden="true" tabindex="-1"></a>personal_help_id2<span class="ot">&lt;-</span><span class="fu">subset</span>(personal_help_id2, personal_help_id2<span class="sc">!=</span><span class="st">&quot;.&quot;</span> <span class="sc">&amp;</span> personal_help_id2<span class="sc">!=</span><span class="st">&quot;NA&quot;</span>)</span>
<span id="cb32-14"><a href="#cb32-14" aria-hidden="true" tabindex="-1"></a>personal_help_id3<span class="ot">&lt;-</span><span class="fu">subset</span>(personal_help_id3, personal_help_id3<span class="sc">!=</span><span class="st">&quot;.&quot;</span> <span class="sc">&amp;</span> personal_help_id3<span class="sc">!=</span><span class="st">&quot;NA&quot;</span>)</span>
<span id="cb32-15"><a href="#cb32-15" aria-hidden="true" tabindex="-1"></a>personal_help_id4<span class="ot">&lt;-</span><span class="fu">subset</span>(personal_help_id4, personal_help_id4<span class="sc">!=</span><span class="st">&quot;.&quot;</span> <span class="sc">&amp;</span> personal_help_id4<span class="sc">!=</span><span class="st">&quot;NA&quot;</span>)</span>
<span id="cb32-16"><a href="#cb32-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-17"><a href="#cb32-17" aria-hidden="true" tabindex="-1"></a><span class="co"># rename</span></span>
<span id="cb32-18"><a href="#cb32-18" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(personal_help_id1)[<span class="fu">names</span>(personal_help_id1) <span class="sc">==</span> <span class="st">&#39;covariates$personal_help_id1&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;q31&#39;</span></span>
<span id="cb32-19"><a href="#cb32-19" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(personal_help_id2)[<span class="fu">names</span>(personal_help_id2) <span class="sc">==</span> <span class="st">&#39;covariates$personal_help_id2&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;q31&#39;</span></span>
<span id="cb32-20"><a href="#cb32-20" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(personal_help_id3)[<span class="fu">names</span>(personal_help_id3) <span class="sc">==</span> <span class="st">&#39;covariates$personal_help_id3&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;q31&#39;</span></span>
<span id="cb32-21"><a href="#cb32-21" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(personal_help_id4)[<span class="fu">names</span>(personal_help_id4) <span class="sc">==</span> <span class="st">&#39;covariates$personal_help_id4&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;q31&#39;</span></span>
<span id="cb32-22"><a href="#cb32-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-23"><a href="#cb32-23" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-24"><a href="#cb32-24" aria-hidden="true" tabindex="-1"></a><span class="co"># append</span></span>
<span id="cb32-25"><a href="#cb32-25" aria-hidden="true" tabindex="-1"></a>total <span class="ot">&lt;-</span> <span class="fu">rbind</span>(personal_help_id1, personal_help_id2,personal_help_id3,personal_help_id4)</span>
<span id="cb32-26"><a href="#cb32-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-27"><a href="#cb32-27" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-28"><a href="#cb32-28" aria-hidden="true" tabindex="-1"></a><span class="co">#aggregate</span></span>
<span id="cb32-29"><a href="#cb32-29" aria-hidden="true" tabindex="-1"></a>total<span class="sc">$</span>value <span class="ot">&lt;-</span> <span class="dv">1</span></span>
<span id="cb32-30"><a href="#cb32-30" aria-hidden="true" tabindex="-1"></a>help_id<span class="ot">&lt;-</span><span class="fu">aggregate</span>(total<span class="sc">$</span>value, <span class="at">by=</span><span class="fu">list</span>(<span class="at">help=</span>total<span class="sc">$</span>q31), <span class="at">FUN=</span>sum)</span>
<span id="cb32-31"><a href="#cb32-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-32"><a href="#cb32-32" aria-hidden="true" tabindex="-1"></a><span class="co"># plot </span></span>
<span id="cb32-33"><a href="#cb32-33" aria-hidden="true" tabindex="-1"></a>fig_help_id<span class="ot">&lt;-</span>help_id <span class="sc">%&gt;%</span></span>
<span id="cb32-34"><a href="#cb32-34" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb32-35"><a href="#cb32-35" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">x=</span><span class="fu">reorder</span>(help, x),<span class="at">y =</span> (x)<span class="sc">/</span><span class="fu">sum</span>(x)),<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>) <span class="sc">+</span></span>
<span id="cb32-36"><a href="#cb32-36" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_y_continuous</span>(<span class="at">labels=</span>scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb32-37"><a href="#cb32-37" aria-hidden="true" tabindex="-1"></a>    <span class="fu">coord_flip</span>() <span class="sc">+</span> </span>
<span id="cb32-38"><a href="#cb32-38" aria-hidden="true" tabindex="-1"></a>    <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Number&#39;</span>,<span class="at">x=</span><span class="st">&#39;Actor&#39;</span>)<span class="sc">+</span></span>
<span id="cb32-39"><a href="#cb32-39" aria-hidden="true" tabindex="-1"></a>    <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb32-40"><a href="#cb32-40" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggtitle</span>(<span class="st">&quot;From whom did you receive help?&quot;</span>)</span>
<span id="cb32-41"><a href="#cb32-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-42"><a href="#cb32-42" aria-hidden="true" tabindex="-1"></a>MP_satisfied <span class="ot">&lt;-</span> covariates <span class="sc">%&gt;%</span></span>
<span id="cb32-43"><a href="#cb32-43" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(satisfied_personal_help <span class="sc">!=</span> <span class="st">&quot;.&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb32-44"><a href="#cb32-44" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(personal_help_id1 <span class="sc">==</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb32-45"><a href="#cb32-45" aria-hidden="true" tabindex="-1"></a>                personal_help_id2 <span class="sc">==</span> <span class="st">&quot;.&quot;</span>,</span>
<span id="cb32-46"><a href="#cb32-46" aria-hidden="true" tabindex="-1"></a>                personal_help_id3 <span class="sc">==</span> <span class="st">&quot;.&quot;</span>,</span>
<span id="cb32-47"><a href="#cb32-47" aria-hidden="true" tabindex="-1"></a>                personal_help_id4 <span class="sc">==</span> <span class="st">&quot;.&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb32-48"><a href="#cb32-48" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">count</span>(satisfied_personal_help) <span class="sc">%&gt;%</span></span>
<span id="cb32-49"><a href="#cb32-49" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">sum_group =</span> n) <span class="sc">%&gt;%</span></span>
<span id="cb32-50"><a href="#cb32-50" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb32-51"><a href="#cb32-51" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">x=</span>satisfied_personal_help,<span class="at">y =</span> (sum_group)<span class="sc">/</span><span class="fu">sum</span>(sum_group)<span class="sc">*</span><span class="dv">100</span>),<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>,<span class="at">width=</span><span class="fl">0.4</span>) <span class="sc">+</span></span>
<span id="cb32-52"><a href="#cb32-52" aria-hidden="true" tabindex="-1"></a>    <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb32-53"><a href="#cb32-53" aria-hidden="true" tabindex="-1"></a>    <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Number of respondents in %&#39;</span>,<span class="at">x=</span><span class="st">&#39;How satisfied were you with the help you received?&#39;</span>)<span class="sc">+</span></span>
<span id="cb32-54"><a href="#cb32-54" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggtitle</span>(<span class="st">&quot;Subset: Respondents who recieved help only from MP&quot;</span>)</span>
<span id="cb32-55"><a href="#cb32-55" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-56"><a href="#cb32-56" aria-hidden="true" tabindex="-1"></a>help_id <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(fig_help_id,MP_satisfied,</span>
<span id="cb32-57"><a href="#cb32-57" aria-hidden="true" tabindex="-1"></a>                    <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>)</span>
<span id="cb32-58"><a href="#cb32-58" aria-hidden="true" tabindex="-1"></a>help_id</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A6_help_id.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="table-a1-summary-statistics-conjoint-experiment" class="level2">
<h2 class="anchored" data-anchor-id="table-a1-summary-statistics-conjoint-experiment">Table A1: Summary Statistics Conjoint Experiment</h2>
<div class="cell-output-display">
<table class="table" data-quarto-postprocess="true">
<caption>Summary Statistics</caption>
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th">Variable</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">N</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Mean</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Std. Dev.</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Min</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Pctl. 25</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Pctl. 75</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Max</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">CaseID</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;">80431290</td>
<td style="text-align: left;">142986</td>
<td style="text-align: left;">80205300</td>
<td style="text-align: left;">80299177</td>
<td style="text-align: left;">80553498</td>
<td style="text-align: left;">80705614</td>
</tr>
<tr class="even">
<td style="text-align: left;">Effort_prevention</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... No Prevention Effort</td>
<td style="text-align: left;">4738</td>
<td style="text-align: left;">49%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... Prevention Effort</td>
<td style="text-align: left;">4922</td>
<td style="text-align: left;">51%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">Effort_relief</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... No Relief Effort</td>
<td style="text-align: left;">4808</td>
<td style="text-align: left;">50%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... Relief Effort</td>
<td style="text-align: left;">4852</td>
<td style="text-align: left;">50%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">Effective_prevention</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... Prevention Not Effective</td>
<td style="text-align: left;">4919</td>
<td style="text-align: left;">51%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... Prevention Effective</td>
<td style="text-align: left;">4741</td>
<td style="text-align: left;">49%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">EQ</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... No Visits</td>
<td style="text-align: left;">4784</td>
<td style="text-align: left;">50%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... Visits</td>
<td style="text-align: left;">4876</td>
<td style="text-align: left;">50%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">Honesty</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... No Corruption</td>
<td style="text-align: left;">3226</td>
<td style="text-align: left;">33%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... Corruption</td>
<td style="text-align: left;">3204</td>
<td style="text-align: left;">33%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... Vote Buying</td>
<td style="text-align: left;">3230</td>
<td style="text-align: left;">33%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">Effective_relief</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... No Relief</td>
<td style="text-align: left;">4821</td>
<td style="text-align: left;">50%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... Relief Effective</td>
<td style="text-align: left;">4839</td>
<td style="text-align: left;">50%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">Ask</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... Not Ask Relief</td>
<td style="text-align: left;">4835</td>
<td style="text-align: left;">50%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... Ask Relief</td>
<td style="text-align: left;">4825</td>
<td style="text-align: left;">50%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">Choosen_Candidate</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;">1.5</td>
<td style="text-align: left;">0.5</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">2</td>
</tr>
<tr class="odd">
<td style="text-align: left;">contest</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;">3.5</td>
<td style="text-align: left;">1.7</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">5</td>
<td style="text-align: left;">6</td>
</tr>
<tr class="even">
<td style="text-align: left;">candidate</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;">1.5</td>
<td style="text-align: left;">0.5</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">2</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Choice</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;">0.5</td>
<td style="text-align: left;">0.5</td>
<td style="text-align: left;">0</td>
<td style="text-align: left;">0</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">hours</td>
<td style="text-align: left;">9660</td>
<td style="text-align: left;">0.37</td>
<td style="text-align: left;">0.17</td>
<td style="text-align: left;">0.17</td>
<td style="text-align: left;">0.29</td>
<td style="text-align: left;">0.41</td>
<td style="text-align: left;">2.6</td>
</tr>
</tbody>
</table>


</div>
</section>
<section id="table-a2-summary-statistics" class="level2">
<h2 class="anchored" data-anchor-id="table-a2-summary-statistics">Table A2: Summary Statistics</h2>
<div class="cell-output-display">
<table class="table" data-quarto-postprocess="true">
<caption>Summary Statistics</caption>
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th">Variable</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">N</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Mean</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Std. Dev.</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Min</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Pctl. 25</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Pctl. 75</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Max</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">CaseID</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">80431290</td>
<td style="text-align: left;">143067</td>
<td style="text-align: left;">80205300</td>
<td style="text-align: left;">80299177</td>
<td style="text-align: left;">80553498</td>
<td style="text-align: left;">80705614</td>
</tr>
<tr class="even">
<td style="text-align: left;">disaster_prime</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... control</td>
<td style="text-align: left;">398</td>
<td style="text-align: left;">49%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... treatment</td>
<td style="text-align: left;">407</td>
<td style="text-align: left;">51%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">education</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">3.2</td>
<td style="text-align: left;">1.4</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">3</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">7</td>
</tr>
<tr class="even">
<td style="text-align: left;">farmer</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">0.95</td>
<td style="text-align: left;">0.21</td>
<td style="text-align: left;">0</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">manipulation</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">4.2</td>
<td style="text-align: left;">1.3</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">5</td>
<td style="text-align: left;">5</td>
</tr>
<tr class="even">
<td style="text-align: left;">income</td>
<td style="text-align: left;">804</td>
<td style="text-align: left;">3.4</td>
<td style="text-align: left;">0.83</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">3</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">4</td>
</tr>
<tr class="odd">
<td style="text-align: left;">age</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">37</td>
<td style="text-align: left;">15</td>
<td style="text-align: left;">18</td>
<td style="text-align: left;">25</td>
<td style="text-align: left;">45</td>
<td style="text-align: left;">96</td>
</tr>
<tr class="even">
<td style="text-align: left;">gender</td>
<td style="text-align: left;">804</td>
<td style="text-align: left;">0.5</td>
<td style="text-align: left;">0.5</td>
<td style="text-align: left;">0</td>
<td style="text-align: left;">0</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">income2</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">1.4</td>
<td style="text-align: left;">0.67</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">4</td>
</tr>
<tr class="even">
<td style="text-align: left;">worried</td>
<td style="text-align: left;">798</td>
<td style="text-align: left;">2.7</td>
<td style="text-align: left;">0.59</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">3</td>
<td style="text-align: left;">3</td>
<td style="text-align: left;">3</td>
</tr>
<tr class="odd">
<td style="text-align: left;">life2015</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">1.9</td>
<td style="text-align: left;">0.33</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">2</td>
</tr>
<tr class="even">
<td style="text-align: left;">incumbent_votingMP</td>
<td style="text-align: left;">797</td>
<td style="text-align: left;">2.1</td>
<td style="text-align: left;">1.3</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">3</td>
<td style="text-align: left;">4</td>
</tr>
<tr class="odd">
<td style="text-align: left;">incumbent_votingVC</td>
<td style="text-align: left;">792</td>
<td style="text-align: left;">2.1</td>
<td style="text-align: left;">1.2</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">3</td>
<td style="text-align: left;">4</td>
</tr>
<tr class="even">
<td style="text-align: left;">interested_politics</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">2.7</td>
<td style="text-align: left;">1.1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">4</td>
</tr>
<tr class="odd">
<td style="text-align: left;">trust_MP</td>
<td style="text-align: left;">799</td>
<td style="text-align: left;">2.4</td>
<td style="text-align: left;">1.3</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">4</td>
</tr>
<tr class="even">
<td style="text-align: left;">flood_econ</td>
<td style="text-align: left;">804</td>
<td style="text-align: left;">3.7</td>
<td style="text-align: left;">1.3</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">5</td>
</tr>
<tr class="odd">
<td style="text-align: left;">flood_psych</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">0.88</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">4</td>
<td style="text-align: left;">5</td>
<td style="text-align: left;">5</td>
</tr>
<tr class="even">
<td style="text-align: left;">personal_help</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">1.2</td>
<td style="text-align: left;">0.41</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
</tr>
<tr class="odd">
<td style="text-align: left;">satisfied_personal_help</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... .</td>
<td style="text-align: left;">636</td>
<td style="text-align: left;">79%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... 1</td>
<td style="text-align: left;">61</td>
<td style="text-align: left;">8%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... 2</td>
<td style="text-align: left;">64</td>
<td style="text-align: left;">8%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... 3</td>
<td style="text-align: left;">29</td>
<td style="text-align: left;">4%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... 4</td>
<td style="text-align: left;">15</td>
<td style="text-align: left;">2%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">disaster_post2015</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">1.4</td>
<td style="text-align: left;">0.48</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">2</td>
</tr>
<tr class="even">
<td style="text-align: left;">personal_help_id3</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... .</td>
<td style="text-align: left;">793</td>
<td style="text-align: left;">99%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... 10</td>
<td style="text-align: left;">5</td>
<td style="text-align: left;">1%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... 11</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">0%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... 9</td>
<td style="text-align: left;">6</td>
<td style="text-align: left;">1%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">personal_help_id4</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... .</td>
<td style="text-align: left;">803</td>
<td style="text-align: left;">100%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... 10</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">0%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">community_help_id3</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... .</td>
<td style="text-align: left;">788</td>
<td style="text-align: left;">98%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... 7</td>
<td style="text-align: left;">5</td>
<td style="text-align: left;">1%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">... 8</td>
<td style="text-align: left;">11</td>
<td style="text-align: left;">1%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">... 9</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">0%</td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
</tr>
<tr class="odd">
<td style="text-align: left;">community_help</td>
<td style="text-align: left;">797</td>
<td style="text-align: left;">1.6</td>
<td style="text-align: left;">0.48</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">2</td>
<td style="text-align: left;">2</td>
</tr>
<tr class="even">
<td style="text-align: left;">distance_flood</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">5156</td>
<td style="text-align: left;">6904</td>
<td style="text-align: left;">0</td>
<td style="text-align: left;">292</td>
<td style="text-align: left;">11071</td>
<td style="text-align: left;">19983</td>
</tr>
<tr class="odd">
<td style="text-align: left;">elevation</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">121</td>
<td style="text-align: left;">66</td>
<td style="text-align: left;">47</td>
<td style="text-align: left;">59</td>
<td style="text-align: left;">191</td>
<td style="text-align: left;">234</td>
</tr>
<tr class="even">
<td style="text-align: left;">hours</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">0.37</td>
<td style="text-align: left;">0.17</td>
<td style="text-align: left;">0.17</td>
<td style="text-align: left;">0.29</td>
<td style="text-align: left;">0.41</td>
<td style="text-align: left;">2.6</td>
</tr>
<tr class="odd">
<td style="text-align: left;">poverty</td>
<td style="text-align: left;">805</td>
<td style="text-align: left;">0.88</td>
<td style="text-align: left;">0.33</td>
<td style="text-align: left;">0</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
<td style="text-align: left;">1</td>
</tr>
</tbody>
</table>


</div>
</section>
<section id="table-a4-main-results" class="level2">
<h2 class="anchored" data-anchor-id="table-a4-main-results">Table A4: Main Results</h2>
<div class="cell-output-display">
<table class="table" data-quarto-postprocess="true">
<caption>Main Results</caption>
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th"></th>
<th style="text-align: center;" data-quarto-table-cell-role="th"> (1)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Intercept</td>
<td style="text-align: center;">0.31***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort</td>
<td style="text-align: center;">0.05***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effort</td>
<td style="text-align: center;">0.09***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effective</td>
<td style="text-align: center;">0.11***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effective</td>
<td style="text-align: center;">0.12***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Ask</td>
<td style="text-align: center;">0.12***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Visits</td>
<td style="text-align: center;">0.16***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Corruption</td>
<td style="text-align: center;">−0.24***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Vote Buying</td>
<td style="text-align: center;">−0.17***</td>
</tr>
<tr class="even">
<td style="text-align: left; box-shadow: 0px 1.5px;"></td>
<td style="text-align: center; box-shadow: 0px 1.5px;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Num.Obs.</td>
<td style="text-align: center;">9660</td>
</tr>
<tr class="even">
<td style="text-align: left;">R2</td>
<td style="text-align: center;">0.117</td>
</tr>
<tr class="odd">
<td style="text-align: left;">R2 Adj.</td>
<td style="text-align: center;">0.116</td>
</tr>
<tr class="even">
<td style="text-align: left;">AIC</td>
<td style="text-align: center;">12843.0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">BIC</td>
<td style="text-align: center;">12914.8</td>
</tr>
<tr class="even">
<td style="text-align: left;">RMSE</td>
<td style="text-align: center;">0.47</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Std.Errors</td>
<td style="text-align: center;">by: CaseID</td>
</tr>
</tbody><tfoot>
<tr class="even">
<td style="text-align: left; padding: 0;"><sup></sup> + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
<td style="text-align: center;"></td>
</tr>
</tfoot>

</table>


</div>
</section>
<section id="figure-a9-marginal-means" class="level2">
<h2 class="anchored" data-anchor-id="figure-a9-marginal-means">Figure A9: Marginal Means</h2>
<pre><code>Warning in logLik.svyglm(x): svyglm not fitted by maximum likelihood.
Warning in logLik.svyglm(x): svyglm not fitted by maximum likelihood.
Warning in logLik.svyglm(x): svyglm not fitted by maximum likelihood.
Warning in logLik.svyglm(x): svyglm not fitted by maximum likelihood.
Warning in logLik.svyglm(x): svyglm not fitted by maximum likelihood.
Warning in logLik.svyglm(x): svyglm not fitted by maximum likelihood.
Warning in logLik.svyglm(x): svyglm not fitted by maximum likelihood.</code></pre>
<p><img 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" class="img-fluid" width="672"></p>
</section>
<section id="table-a5-main-results-linear-hypothesis-1" class="level2">
<h2 class="anchored" data-anchor-id="table-a5-main-results-linear-hypothesis-1">Table A5: Main Results, Linear Hypothesis 1</h2>
<div class="cell-output-display">
<table class="table" data-quarto-postprocess="true">
<caption>Main Results, Linear hypothesis 1</caption>
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th"></th>
<th style="text-align: center;" data-quarto-table-cell-role="th"> (1)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Intercept</td>
<td style="text-align: center;">0.31***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort</td>
<td style="text-align: center;">0.05***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effort</td>
<td style="text-align: center;">0.09***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effective</td>
<td style="text-align: center;">0.11***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effective</td>
<td style="text-align: center;">0.12***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Ask</td>
<td style="text-align: center;">0.12***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Visits</td>
<td style="text-align: center;">0.16***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Corruption</td>
<td style="text-align: center;">−0.24***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Vote Buying</td>
<td style="text-align: center;">−0.17***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort = Relief Effort</td>
<td style="text-align: center;">−0.03**</td>
</tr>
<tr class="even">
<td style="text-align: left; box-shadow: 0px 1.5px;"></td>
<td style="text-align: center; box-shadow: 0px 1.5px;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Num.Obs.</td>
<td style="text-align: center;">9660</td>
</tr>
<tr class="even">
<td style="text-align: left;">R2</td>
<td style="text-align: center;">0.117</td>
</tr>
<tr class="odd">
<td style="text-align: left;">R2 Adj.</td>
<td style="text-align: center;">0.116</td>
</tr>
<tr class="even">
<td style="text-align: left;">Std.Errors</td>
<td style="text-align: center;">HC2</td>
</tr>
</tbody><tfoot>
<tr class="odd">
<td style="text-align: left; padding: 0;"><sup></sup> + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
<td style="text-align: center;"></td>
</tr>
</tfoot>

</table>


</div>
</section>
<section id="table-a6-main-results-linear-hypothesis-2" class="level2">
<h2 class="anchored" data-anchor-id="table-a6-main-results-linear-hypothesis-2">Table A6: Main Results, Linear Hypothesis 2</h2>
<div class="cell-output-display">
<table class="table" data-quarto-postprocess="true">
<caption>Main Results, Linear hypothesis 2</caption>
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th"></th>
<th style="text-align: center;" data-quarto-table-cell-role="th"> (1)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Intercept</td>
<td style="text-align: center;">0.31***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort</td>
<td style="text-align: center;">0.05***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effort</td>
<td style="text-align: center;">0.09***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effective</td>
<td style="text-align: center;">0.11***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effective</td>
<td style="text-align: center;">0.12***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Ask</td>
<td style="text-align: center;">0.12***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Visits</td>
<td style="text-align: center;">0.16***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Corruption</td>
<td style="text-align: center;">−0.24***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Vote Buying</td>
<td style="text-align: center;">−0.17***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effective = Relief Effective</td>
<td style="text-align: center;">−0.01</td>
</tr>
<tr class="even">
<td style="text-align: left; box-shadow: 0px 1.5px;"></td>
<td style="text-align: center; box-shadow: 0px 1.5px;">(0.01)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Num.Obs.</td>
<td style="text-align: center;">9660</td>
</tr>
<tr class="even">
<td style="text-align: left;">R2</td>
<td style="text-align: center;">0.117</td>
</tr>
<tr class="odd">
<td style="text-align: left;">R2 Adj.</td>
<td style="text-align: center;">0.116</td>
</tr>
<tr class="even">
<td style="text-align: left;">Std.Errors</td>
<td style="text-align: center;">HC2</td>
</tr>
</tbody><tfoot>
<tr class="odd">
<td style="text-align: left; padding: 0;"><sup></sup> + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
<td style="text-align: center;"></td>
</tr>
</tfoot>

</table>


</div>
</section>
<section id="table-a7-main-results-with-interactions" class="level2">
<h2 class="anchored" data-anchor-id="table-a7-main-results-with-interactions">Table A7: Main Results with Interactions</h2>
<div class="cell-output-display">
<table class="table" data-quarto-postprocess="true">
<caption>Table A7: Main Results with Interactions</caption>
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th"></th>
<th style="text-align: center;" data-quarto-table-cell-role="th"> (1)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Intercept</td>
<td style="text-align: center;">0.46***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort</td>
<td style="text-align: center;">0.05*</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effort</td>
<td style="text-align: center;">0.09***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effective</td>
<td style="text-align: center;">0.11***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effective</td>
<td style="text-align: center;">0.10***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Corruption</td>
<td style="text-align: center;">−0.24***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Vote Buying</td>
<td style="text-align: center;">−0.18***</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effective × Corruption</td>
<td style="text-align: center;">−0.01</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effective × Vote Buying</td>
<td style="text-align: center;">0.01</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effort × Relief Effective</td>
<td style="text-align: center;">0.01</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effective × Relief Effective</td>
<td style="text-align: center;">−0.01</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort × Relief Effort</td>
<td style="text-align: center;">−0.01</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort × Preparedness Effective</td>
<td style="text-align: center;">0.00</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort × Corruption</td>
<td style="text-align: center;">0.01</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Preparedness Effort × Vote Buying</td>
<td style="text-align: center;">0.00</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effective × Corruption</td>
<td style="text-align: center;">0.02</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: center;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Relief Effective × Vote Buying</td>
<td style="text-align: center;">0.03</td>
</tr>
<tr class="even">
<td style="text-align: left; box-shadow: 0px 1.5px;"></td>
<td style="text-align: center; box-shadow: 0px 1.5px;">(0.02)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Num.Obs.</td>
<td style="text-align: center;">9660</td>
</tr>
<tr class="even">
<td style="text-align: left;">R2</td>
<td style="text-align: center;">0.078</td>
</tr>
<tr class="odd">
<td style="text-align: left;">R2 Adj.</td>
<td style="text-align: center;">0.076</td>
</tr>
<tr class="even">
<td style="text-align: left;">AIC</td>
<td style="text-align: center;">13278.3</td>
</tr>
<tr class="odd">
<td style="text-align: left;">BIC</td>
<td style="text-align: center;">13407.5</td>
</tr>
<tr class="even">
<td style="text-align: left;">RMSE</td>
<td style="text-align: center;">0.48</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Std.Errors</td>
<td style="text-align: center;">by: CaseID</td>
</tr>
</tbody><tfoot>
<tr class="even">
<td style="text-align: left; padding: 0;"><sup></sup> + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
<td style="text-align: center;"></td>
</tr>
</tfoot>

</table>


</div>
</section>
<section id="figure-a10-heterogeneity-of-amce-by-number-of-contest" class="level2">
<h2 class="anchored" data-anchor-id="figure-a10-heterogeneity-of-amce-by-number-of-contest">Figure A10: Heterogeneity of AMCE by Number of Contest</h2>
<p><img src="data:image/png;base64,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" class="img-fluid" width="672"></p>
</section>
<section id="figure-a11-distance-to-flood-2015-in-2018." class="level2">
<h2 class="anchored" data-anchor-id="figure-a11-distance-to-flood-2015-in-2018.">Figure A11: Distance to Flood (2015) in 2018.</h2>
<div class="cell" data-layout-align="center">
<div class="cell-output-display">
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img 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" class="img-fluid figure-img" style="width:60.0%"></p>
<figcaption class="figure-caption">Distance of survey respindents to Flood (2015) in 2018 (in meters)</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="figure-2-amces-by-by-distance-to-2015-flood" class="level2">
<h2 class="anchored" data-anchor-id="figure-2-amces-by-by-distance-to-2015-flood">Figure 2: AMCE’s by by distance to 2015 flood</h2>
<pre><code>Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].
Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].
Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].
Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].
Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].
Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].</code></pre>
<pre><code>Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.</code></pre>
<pre><code>Warning: Removed 42 rows containing missing values or values outside the scale range
(`geom_point()`).</code></pre>
<pre><code>Warning: Removed 84 rows containing missing values or values outside the scale range
(`geom_errorbarh()`).</code></pre>
<p><img 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" class="img-fluid" width="672"></p>
<pre><code>Warning: Removed 42 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 84 rows containing missing values or values outside the scale range
(`geom_errorbarh()`).</code></pre>
<section id="figure-a13-scatterplot-economic-distress-x-and-distance-to-flood-y" class="level3">
<h3 class="anchored" data-anchor-id="figure-a13-scatterplot-economic-distress-x-and-distance-to-flood-y">Figure A13: Scatterplot Economic Distress (X) and Distance to Flood (Y)</h3>
<div class="cell" data-layout-align="center">
<div class="cell-output-display">
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img 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" class="img-fluid figure-img" style="width:50.0%"></p>
<figcaption class="figure-caption">Scatter plot, Distance to the 2015 flood in meters (X-Axis) and seft-reported economic hardship due to the flood (Y-axis)</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="figure-a14-flood-exposure" class="level3">
<h3 class="anchored" data-anchor-id="figure-a14-flood-exposure">Figure A14: Flood exposure</h3>
<div class="cell" data-layout-align="center">
<div class="cell-output-display">
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img 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" class="img-fluid figure-img" style="width:70.0%"></p>
<figcaption class="figure-caption">Distribution: seft-reported economic hardship due to the flood</figcaption>
</figure>
</div>
</div>
</div>
</section>
</section>
<section id="figure-a15-difference-in-amces-and-marginal-means" class="level2">
<h2 class="anchored" data-anchor-id="figure-a15-difference-in-amces-and-marginal-means">Figure A15: Difference in AMCEs and Marginal Means</h2>
<div class="cell" data-layout-align="center">
<div class="cell-output-display">
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img 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" class="img-fluid figure-img" style="width:100.0%"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="figure-a16-balance-tests" class="level2">
<h2 class="anchored" data-anchor-id="figure-a16-balance-tests">Figure A16: Balance Tests</h2>
<div class="cell" data-layout-align="center">
<div class="cell-output-display">
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img 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" class="img-fluid figure-img" style="width:100.0%"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="table-a8-manipulation-check" class="level2">
<h2 class="anchored" data-anchor-id="table-a8-manipulation-check">Table A8: Manipulation Check</h2>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb40"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;1_input/covariates.rds&quot;</span>)</span>
<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a>manipulation<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(manipulation <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb40-4"><a href="#cb40-4" aria-hidden="true" tabindex="-1"></a>resp_MP<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(T_q37_4 <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb40-5"><a href="#cb40-5" aria-hidden="true" tabindex="-1"></a>resp_MP2<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(T_q38_4 <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb40-6"><a href="#cb40-6" aria-hidden="true" tabindex="-1"></a>worried<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(worried <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb40-7"><a href="#cb40-7" aria-hidden="true" tabindex="-1"></a>hopeless<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(T_q24_4 <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb40-8"><a href="#cb40-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb40-9"><a href="#cb40-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Table </span></span>
<span id="cb40-10"><a href="#cb40-10" aria-hidden="true" tabindex="-1"></a>tex_table <span class="ot">&lt;-</span> <span class="fu">texreg</span>(</span>
<span id="cb40-11"><a href="#cb40-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">list</span>(manipulation, resp_MP, resp_MP2, worried, hopeless),</span>
<span id="cb40-12"><a href="#cb40-12" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.ci =</span> <span class="cn">FALSE</span>,</span>
<span id="cb40-13"><a href="#cb40-13" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="fu">c</span>(<span class="fl">0.001</span>, <span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb40-14"><a href="#cb40-14" aria-hidden="true" tabindex="-1"></a>  <span class="at">caption =</span> <span class="st">&quot;Manipulation Check&quot;</span>,</span>
<span id="cb40-15"><a href="#cb40-15" aria-hidden="true" tabindex="-1"></a>  <span class="at">custom.model.names =</span> <span class="fu">c</span>(<span class="st">&quot;Financial Worry&quot;</span>, <span class="st">&quot;MP Responsible Relief&quot;</span>, </span>
<span id="cb40-16"><a href="#cb40-16" aria-hidden="true" tabindex="-1"></a>                         <span class="st">&quot;MP Responsible Preparedness&quot;</span>, <span class="st">&quot;Flood Worry&quot;</span>, <span class="st">&quot;Hopeless&quot;</span>)</span>
<span id="cb40-17"><a href="#cb40-17" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb40-18"><a href="#cb40-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb40-19"><a href="#cb40-19" aria-hidden="true" tabindex="-1"></a><span class="co"># Write the LaTeX table to a file</span></span>
<span id="cb40-20"><a href="#cb40-20" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(tex_table, <span class="at">file =</span> <span class="st">&quot;2_output/tab_A8_manipulation_check.tex&quot;</span>)</span>
<span id="cb40-21"><a href="#cb40-21" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb40-22"><a href="#cb40-22" aria-hidden="true" tabindex="-1"></a><span class="fu">htmlreg</span>(</span>
<span id="cb40-23"><a href="#cb40-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">list</span>(manipulation, resp_MP, resp_MP2, worried, hopeless),</span>
<span id="cb40-24"><a href="#cb40-24" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.ci =</span> <span class="cn">FALSE</span>,</span>
<span id="cb40-25"><a href="#cb40-25" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="fu">c</span>(<span class="fl">0.001</span>, <span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb40-26"><a href="#cb40-26" aria-hidden="true" tabindex="-1"></a>  <span class="at">caption =</span> <span class="st">&quot;Manipulation Check&quot;</span>,</span>
<span id="cb40-27"><a href="#cb40-27" aria-hidden="true" tabindex="-1"></a>  <span class="at">custom.model.names =</span> <span class="fu">c</span>(<span class="st">&quot;Financial Worry&quot;</span>, <span class="st">&quot;MP Responsible Relief&quot;</span>, </span>
<span id="cb40-28"><a href="#cb40-28" aria-hidden="true" tabindex="-1"></a>                         <span class="st">&quot;MP Responsible Preparedness&quot;</span>, <span class="st">&quot;Flood Worry&quot;</span>, <span class="st">&quot;Hopeless&quot;</span>)</span>
<span id="cb40-29"><a href="#cb40-29" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>

<table class="texreg" style="margin: 10px auto;border-collapse: collapse;border-spacing: 0px;caption-side: bottom;color: #000000;border-top: 2px solid #000000;">
<caption>
Manipulation Check
</caption>
<thead>
<tr>
<th style="padding-left: 5px;padding-right: 5px;">
 
</th>
<th style="padding-left: 5px;padding-right: 5px;">
Financial Worry
</th>
<th style="padding-left: 5px;padding-right: 5px;">
MP Responsible Relief
</th>
<th style="padding-left: 5px;padding-right: 5px;">
MP Responsible Preparedness
</th>
<th style="padding-left: 5px;padding-right: 5px;">
Flood Worry
</th>
<th style="padding-left: 5px;padding-right: 5px;">
Hopeless
</th>
</tr>
</thead>
<tbody>
<tr style="border-top: 1px solid #000000;">
<td style="padding-left: 5px;padding-right: 5px;">
(Intercept)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
4.13<sup>***</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
2.63<sup>***</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
2.57<sup>***</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
2.69<sup>***</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
2.30<sup>***</sup>
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.07)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.04)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.07)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
disaster_primetreatment
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.18<sup>·</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.06
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.10
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.09)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.05)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.05)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.04)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.10)
</td>
</tr>
<tr style="border-top: 1px solid #000000;">
<td style="padding-left: 5px;padding-right: 5px;">
R<sup>2</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Adj. R<sup>2</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Num. obs.
</td>
<td style="padding-left: 5px;padding-right: 5px;">
805
</td>
<td style="padding-left: 5px;padding-right: 5px;">
801
</td>
<td style="padding-left: 5px;padding-right: 5px;">
802
</td>
<td style="padding-left: 5px;padding-right: 5px;">
798
</td>
<td style="padding-left: 5px;padding-right: 5px;">
805
</td>
</tr>
<tr style="border-bottom: 2px solid #000000;">
<td style="padding-left: 5px;padding-right: 5px;">
RMSE
</td>
<td style="padding-left: 5px;padding-right: 5px;">
1.28
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.65
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.71
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.59
</td>
<td style="padding-left: 5px;padding-right: 5px;">
1.36
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="font-size: 0.8em;" colspan="6">
<sup>***</sup>p &lt; 0.001; <sup>**</sup>p &lt; 0.01; <sup>*</sup>p &lt; 0.05; <sup>·</sup>p &lt; 0.1
</td>
</tr>
</tfoot>

</table>
</section>
<section id="figure-a17-difference-in-amces-of-attributes-on-candidate-support-by-disaster-prime" class="level2">
<h2 class="anchored" data-anchor-id="figure-a17-difference-in-amces-of-attributes-on-candidate-support-by-disaster-prime">Figure A17: Difference in AMCEs of Attributes on Candidate Support, by disaster prime</h2>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb41"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>  df_long_main <span class="ot">&lt;-</span> <span class="fu">left_join</span>(df_long,covariates, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;CaseID&quot;</span>)) </span>
<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Prime</span></span>
<span id="cb41-4"><a href="#cb41-4" aria-hidden="true" tabindex="-1"></a>prime<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>Ask<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>disaster_prime<span class="sc">+</span>Effort_prevention<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb41-5"><a href="#cb41-5" aria-hidden="true" tabindex="-1"></a>                   Effort_relief<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb41-6"><a href="#cb41-6" aria-hidden="true" tabindex="-1"></a>                   Effective_prevention<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb41-7"><a href="#cb41-7" aria-hidden="true" tabindex="-1"></a>                   Effective_relief<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb41-8"><a href="#cb41-8" aria-hidden="true" tabindex="-1"></a>                   Ask<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb41-9"><a href="#cb41-9" aria-hidden="true" tabindex="-1"></a>                   EQ<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb41-10"><a href="#cb41-10" aria-hidden="true" tabindex="-1"></a>                   Honesty<span class="sc">*</span>disaster_prime,</span>
<span id="cb41-11"><a href="#cb41-11" aria-hidden="true" tabindex="-1"></a>                   <span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID) <span class="sc">%&gt;%</span> <span class="fu">tidy</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb41-12"><a href="#cb41-12" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="fu">grepl</span>(<span class="st">&#39;:&#39;</span>, term)) <span class="sc">%&gt;%</span> </span>
<span id="cb41-13"><a href="#cb41-13" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">term=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(term, </span>
<span id="cb41-14"><a href="#cb41-14" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effort_preventionPrevention Effort:disaster_primetreatment&quot;</span><span class="ot">=</span><span class="st">&quot;Preparedness Effort&quot;</span>, </span>
<span id="cb41-15"><a href="#cb41-15" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effort_reliefRelief Effort:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort&quot;</span>,</span>
<span id="cb41-16"><a href="#cb41-16" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effective_preventionPrevention Effective:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effevtive&quot;</span>,</span>
<span id="cb41-17"><a href="#cb41-17" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effective_reliefRelief Effective:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effevtive&quot;</span>,</span>
<span id="cb41-18"><a href="#cb41-18" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;AskAsk Relief:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Ask Help&quot;</span>,</span>
<span id="cb41-19"><a href="#cb41-19" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EQVisits:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb41-20"><a href="#cb41-20" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;HonestyCorruption:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span>,</span>
<span id="cb41-21"><a href="#cb41-21" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;HonestyVote Buying:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying&quot;</span></span>
<span id="cb41-22"><a href="#cb41-22" aria-hidden="true" tabindex="-1"></a>                     )</span>
<span id="cb41-23"><a href="#cb41-23" aria-hidden="true" tabindex="-1"></a>         )<span class="sc">%&gt;%</span> </span>
<span id="cb41-24"><a href="#cb41-24" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb41-25"><a href="#cb41-25" aria-hidden="true" tabindex="-1"></a>  <span class="at">group =</span> <span class="fu">case_when</span>(</span>
<span id="cb41-26"><a href="#cb41-26" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Preparedness Effort&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effort&quot;</span>,</span>
<span id="cb41-27"><a href="#cb41-27" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Relief Effort&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effort&quot;</span>,</span>
<span id="cb41-28"><a href="#cb41-28" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Preparedness Effevtive&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effevtive&quot;</span>,</span>
<span id="cb41-29"><a href="#cb41-29" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Relief Effevtive&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effevtive&quot;</span>,</span>
<span id="cb41-30"><a href="#cb41-30" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Ask Help&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb41-31"><a href="#cb41-31" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Visits&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb41-32"><a href="#cb41-32" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Corruption&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb41-33"><a href="#cb41-33" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Vote Buying&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb41-34"><a href="#cb41-34" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb41-35"><a href="#cb41-35" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb41-36"><a href="#cb41-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb41-37"><a href="#cb41-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb41-38"><a href="#cb41-38" aria-hidden="true" tabindex="-1"></a>fig_prime<span class="ot">&lt;-</span>prime<span class="sc">%&gt;%</span> </span>
<span id="cb41-39"><a href="#cb41-39" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">term =</span> <span class="fu">fct_reorder</span>(term, <span class="fu">desc</span>(<span class="sc">-</span>estimate))) <span class="sc">%&gt;%</span></span>
<span id="cb41-40"><a href="#cb41-40" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> term)) <span class="sc">+</span><span class="co"># color = as.factor(group)</span></span>
<span id="cb41-41"><a href="#cb41-41" aria-hidden="true" tabindex="-1"></a>      <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb41-42"><a href="#cb41-42" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> term, </span>
<span id="cb41-43"><a href="#cb41-43" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span>conf.low, </span>
<span id="cb41-44"><a href="#cb41-44" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> conf.high),</span>
<span id="cb41-45"><a href="#cb41-45" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb41-46"><a href="#cb41-46" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_colour_viridis_d(name = &#39;Group&#39;) + </span></span>
<span id="cb41-47"><a href="#cb41-47" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb41-48"><a href="#cb41-48" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Disaster Prime&quot;</span>)<span class="sc">+</span></span>
<span id="cb41-49"><a href="#cb41-49" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.27</span>, <span class="fl">0.2</span>))<span class="sc">+</span></span>
<span id="cb41-50"><a href="#cb41-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y=</span><span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb41-51"><a href="#cb41-51" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb41-52"><a href="#cb41-52" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb41-53"><a href="#cb41-53" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb41-54"><a href="#cb41-54" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)</span>
<span id="cb41-55"><a href="#cb41-55" aria-hidden="true" tabindex="-1"></a>fig_prime</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<p><img src="data:image/png;base64,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" class="img-fluid" width="672"></p>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A17_prime.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</section>
<section id="table-a9-effect-of-economic-losses-full-models-with-controls" class="level2">
<h2 class="anchored" data-anchor-id="table-a9-effect-of-economic-losses-full-models-with-controls">Table A9: Effect of Economic Losses, Full Models, with Controls</h2>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb43"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>  df_long_main <span class="ot">&lt;-</span> <span class="fu">left_join</span>(df_long,covariates, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;CaseID&quot;</span>)) </span>
<span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-3"><a href="#cb43-3" aria-hidden="true" tabindex="-1"></a>  df_long_main<span class="ot">&lt;-</span>df_long_main <span class="sc">%&gt;%</span></span>
<span id="cb43-4"><a href="#cb43-4" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb43-5"><a href="#cb43-5" aria-hidden="true" tabindex="-1"></a>  <span class="co"># make binary and character</span></span>
<span id="cb43-6"><a href="#cb43-6" aria-hidden="true" tabindex="-1"></a>  <span class="at">flood_econ_bin =</span> <span class="fu">case_when</span>(flood_econ <span class="sc">&lt;=</span><span class="dv">3</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb43-7"><a href="#cb43-7" aria-hidden="true" tabindex="-1"></a>                             flood_econ <span class="sc">&gt;</span><span class="dv">3</span> <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb43-8"><a href="#cb43-8" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb43-9"><a href="#cb43-9" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb43-10"><a href="#cb43-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Economic Distress</span></span>
<span id="cb43-11"><a href="#cb43-11" aria-hidden="true" tabindex="-1"></a>econ_distress1<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb43-12"><a href="#cb43-12" aria-hidden="true" tabindex="-1"></a><span class="co"># controlling for poverty</span></span>
<span id="cb43-13"><a href="#cb43-13" aria-hidden="true" tabindex="-1"></a>econ_distress2<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb43-14"><a href="#cb43-14" aria-hidden="true" tabindex="-1"></a><span class="co"># controlling for help recieved</span></span>
<span id="cb43-15"><a href="#cb43-15" aria-hidden="true" tabindex="-1"></a>econ_distress3<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb43-16"><a href="#cb43-16" aria-hidden="true" tabindex="-1"></a><span class="co"># controlling for satisfied with help </span></span>
<span id="cb43-17"><a href="#cb43-17" aria-hidden="true" tabindex="-1"></a>econ_distress3<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb43-18"><a href="#cb43-18" aria-hidden="true" tabindex="-1"></a><span class="co"># standard covariates</span></span>
<span id="cb43-19"><a href="#cb43-19" aria-hidden="true" tabindex="-1"></a>econ_distress4<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty<span class="sc">+</span>education<span class="sc">+</span>age<span class="sc">+</span>gender<span class="sc">+</span>interested_politics<span class="sc">+</span>trust_MP,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb43-20"><a href="#cb43-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-21"><a href="#cb43-21" aria-hidden="true" tabindex="-1"></a>econ_distress5<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty<span class="sc">+</span>education<span class="sc">+</span>age<span class="sc">+</span>gender<span class="sc">+</span>interested_politics<span class="sc">+</span>trust_MP<span class="sc">+</span>worried<span class="sc">+</span>personal_help<span class="sc">+</span>poverty<span class="sc">*</span>flood_econ_bin,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb43-22"><a href="#cb43-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-23"><a href="#cb43-23" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-24"><a href="#cb43-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the custom coefficient names for renaming and reordering</span></span>
<span id="cb43-25"><a href="#cb43-25" aria-hidden="true" tabindex="-1"></a>coef_names <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb43-26"><a href="#cb43-26" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Effort_preventionPrevention Effort:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Prevention Effort x Economic Losses&quot;</span>,</span>
<span id="cb43-27"><a href="#cb43-27" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Effort_reliefRelief Effort:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort x Economic Losses&quot;</span>,</span>
<span id="cb43-28"><a href="#cb43-28" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;AskAsk Relief:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Ask x Economic Losses&quot;</span>,</span>
<span id="cb43-29"><a href="#cb43-29" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Effective_preventionPrevention Effective:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Prevention Effective x Economic Losses&quot;</span>,</span>
<span id="cb43-30"><a href="#cb43-30" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Effective_reliefRelief Effective:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effective x Economic Losses&quot;</span>,</span>
<span id="cb43-31"><a href="#cb43-31" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;EQVisits:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits x Economic Losses&quot;</span>,</span>
<span id="cb43-32"><a href="#cb43-32" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;HonestyCorruption:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption x Economic Losses&quot;</span>,</span>
<span id="cb43-33"><a href="#cb43-33" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;HonestyVote Buying:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying x Economic Losses&quot;</span>,</span>
<span id="cb43-34"><a href="#cb43-34" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;poverty&quot;</span> <span class="ot">=</span> <span class="st">&quot;Poverty&quot;</span>,</span>
<span id="cb43-35"><a href="#cb43-35" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;education&quot;</span> <span class="ot">=</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb43-36"><a href="#cb43-36" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;age&quot;</span> <span class="ot">=</span> <span class="st">&quot;Age&quot;</span>,</span>
<span id="cb43-37"><a href="#cb43-37" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;gender&quot;</span> <span class="ot">=</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb43-38"><a href="#cb43-38" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;interested_politics&quot;</span> <span class="ot">=</span> <span class="st">&quot;Interested Politics&quot;</span>,</span>
<span id="cb43-39"><a href="#cb43-39" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;trust_MP&quot;</span> <span class="ot">=</span> <span class="st">&quot;Trust MP&quot;</span>,</span>
<span id="cb43-40"><a href="#cb43-40" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;worried&quot;</span> <span class="ot">=</span> <span class="st">&quot;Flood Worried&quot;</span>,</span>
<span id="cb43-41"><a href="#cb43-41" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;personal_help&quot;</span> <span class="ot">=</span> <span class="st">&quot;Received Help&quot;</span>,</span>
<span id="cb43-42"><a href="#cb43-42" aria-hidden="true" tabindex="-1"></a>   <span class="st">&quot;flood_econ_bin:poverty&quot;</span> <span class="ot">=</span> <span class="st">&quot;Economic Losses x Poverty&quot;</span></span>
<span id="cb43-43"><a href="#cb43-43" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb43-44"><a href="#cb43-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-45"><a href="#cb43-45" aria-hidden="true" tabindex="-1"></a><span class="co"># Create the regression table with renamed and selected coefficients</span></span>
<span id="cb43-46"><a href="#cb43-46" aria-hidden="true" tabindex="-1"></a>tab_econ_losses<span class="ot">&lt;-</span><span class="fu">texreg</span>(</span>
<span id="cb43-47"><a href="#cb43-47" aria-hidden="true" tabindex="-1"></a>  <span class="fu">list</span>(econ_distress1, econ_distress2, econ_distress3, econ_distress4, econ_distress5),</span>
<span id="cb43-48"><a href="#cb43-48" aria-hidden="true" tabindex="-1"></a>  <span class="at">caption =</span> <span class="st">&quot;Effect of Economic Losses, Full Models, with Controls&quot;</span>,</span>
<span id="cb43-49"><a href="#cb43-49" aria-hidden="true" tabindex="-1"></a>  <span class="at">custom.coef.map =</span> coef_names,</span>
<span id="cb43-50"><a href="#cb43-50" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.ci =</span> <span class="cn">FALSE</span>,</span>
<span id="cb43-51"><a href="#cb43-51" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="fu">c</span>(<span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb43-52"><a href="#cb43-52" aria-hidden="true" tabindex="-1"></a>  <span class="at">dcolumn =</span> <span class="cn">FALSE</span>,</span>
<span id="cb43-53"><a href="#cb43-53" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.adjrs =</span> <span class="cn">FALSE</span>,</span>
<span id="cb43-54"><a href="#cb43-54" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.bic =</span> <span class="cn">TRUE</span>,</span>
<span id="cb43-55"><a href="#cb43-55" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.rs =</span> <span class="cn">TRUE</span>,</span>
<span id="cb43-56"><a href="#cb43-56" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.rmse =</span> <span class="cn">FALSE</span></span>
<span id="cb43-57"><a href="#cb43-57" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb43-58"><a href="#cb43-58" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-59"><a href="#cb43-59" aria-hidden="true" tabindex="-1"></a><span class="co"># Write the LaTeX table to a file</span></span>
<span id="cb43-60"><a href="#cb43-60" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(tab_econ_losses, <span class="at">file =</span> <span class="st">&quot;2_output/tab_A9_econ_losses.tex&quot;</span>)</span>
<span id="cb43-61"><a href="#cb43-61" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-62"><a href="#cb43-62" aria-hidden="true" tabindex="-1"></a><span class="fu">htmlreg</span>(</span>
<span id="cb43-63"><a href="#cb43-63" aria-hidden="true" tabindex="-1"></a>  <span class="fu">list</span>(econ_distress1, econ_distress2, econ_distress3, econ_distress4, econ_distress5),</span>
<span id="cb43-64"><a href="#cb43-64" aria-hidden="true" tabindex="-1"></a>  <span class="at">caption =</span> <span class="st">&quot;Effect of Economic Losses, Full Models, with Controls&quot;</span>,</span>
<span id="cb43-65"><a href="#cb43-65" aria-hidden="true" tabindex="-1"></a>  <span class="at">custom.coef.map =</span> coef_names,</span>
<span id="cb43-66"><a href="#cb43-66" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.ci =</span> <span class="cn">FALSE</span>,</span>
<span id="cb43-67"><a href="#cb43-67" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="fu">c</span>(<span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb43-68"><a href="#cb43-68" aria-hidden="true" tabindex="-1"></a>  <span class="at">dcolumn =</span> <span class="cn">FALSE</span>,</span>
<span id="cb43-69"><a href="#cb43-69" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.adjrs =</span> <span class="cn">FALSE</span>,</span>
<span id="cb43-70"><a href="#cb43-70" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.bic =</span> <span class="cn">TRUE</span>,</span>
<span id="cb43-71"><a href="#cb43-71" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.rs =</span> <span class="cn">TRUE</span>,</span>
<span id="cb43-72"><a href="#cb43-72" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.rmse =</span> <span class="cn">FALSE</span></span>
<span id="cb43-73"><a href="#cb43-73" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>

<table class="texreg" style="margin: 10px auto;border-collapse: collapse;border-spacing: 0px;caption-side: bottom;color: #000000;border-top: 2px solid #000000;">
<caption>
Effect of Economic Losses, Full Models, with Controls
</caption>
<thead>
<tr>
<th style="padding-left: 5px;padding-right: 5px;">
 
</th>
<th style="padding-left: 5px;padding-right: 5px;">
Model 1
</th>
<th style="padding-left: 5px;padding-right: 5px;">
Model 2
</th>
<th style="padding-left: 5px;padding-right: 5px;">
Model 3
</th>
<th style="padding-left: 5px;padding-right: 5px;">
Model 4
</th>
<th style="padding-left: 5px;padding-right: 5px;">
Model 5
</th>
</tr>
</thead>
<tbody>
<tr style="border-top: 1px solid #000000;">
<td style="padding-left: 5px;padding-right: 5px;">
Prevention Effort x Economic Losses
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Relief Effort x Economic Losses
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.01
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.01
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.01
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.01
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.01
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Relief Ask x Economic Losses
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.06<sup>***</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.06<sup>***</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.06<sup>***</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.05<sup>**</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.06<sup>**</sup>
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Prevention Effective x Economic Losses
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.03
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.03
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.03
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.03
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.03
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Relief Effective x Economic Losses
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04<sup>*</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04<sup>*</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04<sup>*</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04<sup>*</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04<sup>*</sup>
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Visits x Economic Losses
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.02
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Corruption x Economic Losses
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.03
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.04
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Vote Buying x Economic Losses
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.07<sup>**</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.07<sup>**</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.07<sup>**</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.06<sup>**</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.06<sup>**</sup>
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.03)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Poverty
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.01
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.01)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.01)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.01)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.01)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Education
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Age
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00<sup>***</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00<sup>**</sup>
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Gender
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.01
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.01
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Interested Politics
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Trust MP
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Flood Worried
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.00
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Received Help
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.00
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.00)
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Economic Losses x Poverty
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
-0.01
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
 
</td>
<td style="padding-left: 5px;padding-right: 5px;">
(0.02)
</td>
</tr>
<tr style="border-top: 1px solid #000000;">
<td style="padding-left: 5px;padding-right: 5px;">
R<sup>2</sup>
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.12
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.12
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.12
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.12
</td>
<td style="padding-left: 5px;padding-right: 5px;">
0.12
</td>
</tr>
<tr>
<td style="padding-left: 5px;padding-right: 5px;">
Num. obs.
</td>
<td style="padding-left: 5px;padding-right: 5px;">
9648
</td>
<td style="padding-left: 5px;padding-right: 5px;">
9648
</td>
<td style="padding-left: 5px;padding-right: 5px;">
9648
</td>
<td style="padding-left: 5px;padding-right: 5px;">
9564
</td>
<td style="padding-left: 5px;padding-right: 5px;">
9480
</td>
</tr>
<tr style="border-bottom: 2px solid #000000;">
<td style="padding-left: 5px;padding-right: 5px;">
N Clusters
</td>
<td style="padding-left: 5px;padding-right: 5px;">
804
</td>
<td style="padding-left: 5px;padding-right: 5px;">
804
</td>
<td style="padding-left: 5px;padding-right: 5px;">
804
</td>
<td style="padding-left: 5px;padding-right: 5px;">
797
</td>
<td style="padding-left: 5px;padding-right: 5px;">
790
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="font-size: 0.8em;" colspan="6">
<sup>***</sup>p &lt; 0.01; <sup>**</sup>p &lt; 0.05; <sup>*</sup>p &lt; 0.1
</td>
</tr>
</tfoot>

</table>
</section>
<section id="figure-a18-media-coverage.-2017-2024-source-gdelt" class="level2">
<h2 class="anchored" data-anchor-id="figure-a18-media-coverage.-2017-2024-source-gdelt">Figure A18: Media coverage. 2017-2024, Source: GDELT</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb44"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a><span class="co"># script base on: https://rpubs.com/drkblake/1007662</span></span>
<span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readr)</span>
<span id="cb44-3"><a href="#cb44-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb44-4"><a href="#cb44-4" aria-hidden="true" tabindex="-1"></a>startdate <span class="ot">&lt;-</span> <span class="st">&quot;20170101&quot;</span></span>
<span id="cb44-5"><a href="#cb44-5" aria-hidden="true" tabindex="-1"></a>enddate <span class="ot">&lt;-</span> <span class="st">&quot;20231231&quot;</span></span>
<span id="cb44-6"><a href="#cb44-6" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb44-7"><a href="#cb44-7" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb44-8"><a href="#cb44-8" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb44-9"><a href="#cb44-9" aria-hidden="true" tabindex="-1"></a><span class="co"># 1. government </span></span>
<span id="cb44-10"><a href="#cb44-10" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb44-11"><a href="#cb44-11" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb44-12"><a href="#cb44-12" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb44-13"><a href="#cb44-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb44-14"><a href="#cb44-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb44-15"><a href="#cb44-15" aria-hidden="true" tabindex="-1"></a><span class="do">### preparedness</span></span>
<span id="cb44-16"><a href="#cb44-16" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;government preparedness&#39;&quot;</span></span>
<span id="cb44-17"><a href="#cb44-17" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb44-18"><a href="#cb44-18" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb44-19"><a href="#cb44-19" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb44-20"><a href="#cb44-20" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb44-21"><a href="#cb44-21" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb44-22"><a href="#cb44-22" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb44-23"><a href="#cb44-23" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb44-24"><a href="#cb44-24" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;government%20preparedness&#39;&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb46"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-3"><a href="#cb46-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb46-4"><a href="#cb46-4" aria-hidden="true" tabindex="-1"></a>Volume_preparedness <span class="ot">&lt;-</span> Volume</span>
<span id="cb46-5"><a href="#cb46-5" aria-hidden="true" tabindex="-1"></a>Volume_preparedness<span class="sc">$</span>preparednessVolume <span class="ot">&lt;-</span> Volume_preparedness<span class="sc">$</span>Value</span>
<span id="cb46-6"><a href="#cb46-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-7"><a href="#cb46-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-8"><a href="#cb46-8" aria-hidden="true" tabindex="-1"></a><span class="do">###  relief</span></span>
<span id="cb46-9"><a href="#cb46-9" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;government relief&#39;&quot;</span></span>
<span id="cb46-10"><a href="#cb46-10" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb46-11"><a href="#cb46-11" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb46-12"><a href="#cb46-12" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb46-13"><a href="#cb46-13" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb46-14"><a href="#cb46-14" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb46-15"><a href="#cb46-15" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb46-16"><a href="#cb46-16" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb46-17"><a href="#cb46-17" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;government%20relief&#39;&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb48"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-3"><a href="#cb48-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb48-4"><a href="#cb48-4" aria-hidden="true" tabindex="-1"></a>Volume_relief <span class="ot">&lt;-</span> Volume</span>
<span id="cb48-5"><a href="#cb48-5" aria-hidden="true" tabindex="-1"></a>Volume_relief<span class="sc">$</span>reliefVolume <span class="ot">&lt;-</span> Volume_relief<span class="sc">$</span>Value</span>
<span id="cb48-6"><a href="#cb48-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-7"><a href="#cb48-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-8"><a href="#cb48-8" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb48-9"><a href="#cb48-9" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_preparedness, Volume_relief, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb48-10"><a href="#cb48-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-11"><a href="#cb48-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-12"><a href="#cb48-12" aria-hidden="true" tabindex="-1"></a>ww_overview<span class="ot">&lt;-</span>Volume_total <span class="sc">%&gt;%</span></span>
<span id="cb48-13"><a href="#cb48-13" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,preparednessVolume,reliefVolume)<span class="sc">%&gt;%</span></span>
<span id="cb48-14"><a href="#cb48-14" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb48-15"><a href="#cb48-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb48-16"><a href="#cb48-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable)) <span class="sc">+</span></span>
<span id="cb48-17"><a href="#cb48-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb48-18"><a href="#cb48-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb48-19"><a href="#cb48-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb48-20"><a href="#cb48-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb48-21"><a href="#cb48-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb48-22"><a href="#cb48-22" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb48-23"><a href="#cb48-23" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb48-24"><a href="#cb48-24" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb48-25"><a href="#cb48-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;government preparedness&quot;</span>, <span class="st">&quot;government relief&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb48-26"><a href="#cb48-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, world-wide&quot;</span>)</span>
<span id="cb48-27"><a href="#cb48-27" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-28"><a href="#cb48-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-29"><a href="#cb48-29" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb48-30"><a href="#cb48-30" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb48-31"><a href="#cb48-31" aria-hidden="true" tabindex="-1"></a><span class="co"># Malawi MI</span></span>
<span id="cb48-32"><a href="#cb48-32" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb48-33"><a href="#cb48-33" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb48-34"><a href="#cb48-34" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-35"><a href="#cb48-35" aria-hidden="true" tabindex="-1"></a><span class="do">###  preparedness</span></span>
<span id="cb48-36"><a href="#cb48-36" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;government preparedness&#39; SourceCountry:MI&quot;</span></span>
<span id="cb48-37"><a href="#cb48-37" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb48-38"><a href="#cb48-38" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb48-39"><a href="#cb48-39" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=TimelineVol&amp;startdatetime=&quot;</span></span>
<span id="cb48-40"><a href="#cb48-40" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb48-41"><a href="#cb48-41" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb48-42"><a href="#cb48-42" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb48-43"><a href="#cb48-43" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb48-44"><a href="#cb48-44" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;government%20preparedness&#39;%20SourceCountry:MI&amp;mode=TimelineVol&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb50"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb50-2"><a href="#cb50-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb50-3"><a href="#cb50-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb50-4"><a href="#cb50-4" aria-hidden="true" tabindex="-1"></a>Volume_preparedness <span class="ot">&lt;-</span> Volume</span>
<span id="cb50-5"><a href="#cb50-5" aria-hidden="true" tabindex="-1"></a>Volume_preparedness<span class="sc">$</span>preparednessVolume <span class="ot">&lt;-</span> Volume_preparedness<span class="sc">$</span>Value</span>
<span id="cb50-6"><a href="#cb50-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb50-7"><a href="#cb50-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb50-8"><a href="#cb50-8" aria-hidden="true" tabindex="-1"></a><span class="do">###  relief</span></span>
<span id="cb50-9"><a href="#cb50-9" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;government relief&#39; SourceCountry:MI&quot;</span></span>
<span id="cb50-10"><a href="#cb50-10" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb50-11"><a href="#cb50-11" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb50-12"><a href="#cb50-12" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=TimelineVol&amp;startdatetime=&quot;</span></span>
<span id="cb50-13"><a href="#cb50-13" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb50-14"><a href="#cb50-14" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb50-15"><a href="#cb50-15" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb50-16"><a href="#cb50-16" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb50-17"><a href="#cb50-17" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;government%20relief&#39;%20SourceCountry:MI&amp;mode=TimelineVol&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb52"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb52-2"><a href="#cb52-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-3"><a href="#cb52-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb52-4"><a href="#cb52-4" aria-hidden="true" tabindex="-1"></a>Volume_relief <span class="ot">&lt;-</span> Volume</span>
<span id="cb52-5"><a href="#cb52-5" aria-hidden="true" tabindex="-1"></a>Volume_relief<span class="sc">$</span>reliefVolume <span class="ot">&lt;-</span> Volume_relief<span class="sc">$</span>Value</span>
<span id="cb52-6"><a href="#cb52-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-7"><a href="#cb52-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-8"><a href="#cb52-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-9"><a href="#cb52-9" aria-hidden="true" tabindex="-1"></a>Volume_test <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_relief, Volume_preparedness, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb52-10"><a href="#cb52-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-11"><a href="#cb52-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-12"><a href="#cb52-12" aria-hidden="true" tabindex="-1"></a>mi_overview<span class="ot">&lt;-</span>Volume_test <span class="sc">%&gt;%</span></span>
<span id="cb52-13"><a href="#cb52-13" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,preparednessVolume,reliefVolume)<span class="sc">%&gt;%</span></span>
<span id="cb52-14"><a href="#cb52-14" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb52-15"><a href="#cb52-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb52-16"><a href="#cb52-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable,<span class="at">alpha=</span><span class="fl">0.3</span>)) <span class="sc">+</span></span>
<span id="cb52-17"><a href="#cb52-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb52-18"><a href="#cb52-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb52-19"><a href="#cb52-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb52-20"><a href="#cb52-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb52-21"><a href="#cb52-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb52-22"><a href="#cb52-22" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb52-23"><a href="#cb52-23" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb52-24"><a href="#cb52-24" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb52-25"><a href="#cb52-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;government preparedness&quot;</span>, <span class="st">&quot;government relief&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb52-26"><a href="#cb52-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, Malawi&quot;</span>)</span>
<span id="cb52-27"><a href="#cb52-27" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-28"><a href="#cb52-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-29"><a href="#cb52-29" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-30"><a href="#cb52-30" aria-hidden="true" tabindex="-1"></a>news_pre_rel1<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(ww_overview,mi_overview,</span>
<span id="cb52-31"><a href="#cb52-31" aria-hidden="true" tabindex="-1"></a>                      <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>,<span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb52-32"><a href="#cb52-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-33"><a href="#cb52-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-34"><a href="#cb52-34" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb52-35"><a href="#cb52-35" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb52-36"><a href="#cb52-36" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb52-37"><a href="#cb52-37" aria-hidden="true" tabindex="-1"></a><span class="co"># 2. disaster </span></span>
<span id="cb52-38"><a href="#cb52-38" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb52-39"><a href="#cb52-39" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb52-40"><a href="#cb52-40" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb52-41"><a href="#cb52-41" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb52-42"><a href="#cb52-42" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-43"><a href="#cb52-43" aria-hidden="true" tabindex="-1"></a><span class="do">### preparedness</span></span>
<span id="cb52-44"><a href="#cb52-44" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;disaster preparedness&#39;&quot;</span></span>
<span id="cb52-45"><a href="#cb52-45" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb52-46"><a href="#cb52-46" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb52-47"><a href="#cb52-47" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb52-48"><a href="#cb52-48" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb52-49"><a href="#cb52-49" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb52-50"><a href="#cb52-50" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb52-51"><a href="#cb52-51" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb52-52"><a href="#cb52-52" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;disaster%20preparedness&#39;&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb54"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb54-2"><a href="#cb54-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb54-3"><a href="#cb54-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb54-4"><a href="#cb54-4" aria-hidden="true" tabindex="-1"></a>Volume_preparedness <span class="ot">&lt;-</span> Volume</span>
<span id="cb54-5"><a href="#cb54-5" aria-hidden="true" tabindex="-1"></a>Volume_preparedness<span class="sc">$</span>preparednessVolume <span class="ot">&lt;-</span> Volume_preparedness<span class="sc">$</span>Value</span>
<span id="cb54-6"><a href="#cb54-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb54-7"><a href="#cb54-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb54-8"><a href="#cb54-8" aria-hidden="true" tabindex="-1"></a><span class="do">###  relief</span></span>
<span id="cb54-9"><a href="#cb54-9" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;disaster relief&#39;&quot;</span></span>
<span id="cb54-10"><a href="#cb54-10" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb54-11"><a href="#cb54-11" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb54-12"><a href="#cb54-12" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb54-13"><a href="#cb54-13" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb54-14"><a href="#cb54-14" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb54-15"><a href="#cb54-15" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb54-16"><a href="#cb54-16" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb54-17"><a href="#cb54-17" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;disaster%20relief&#39;&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb56"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb56-2"><a href="#cb56-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-3"><a href="#cb56-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb56-4"><a href="#cb56-4" aria-hidden="true" tabindex="-1"></a>Volume_relief <span class="ot">&lt;-</span> Volume</span>
<span id="cb56-5"><a href="#cb56-5" aria-hidden="true" tabindex="-1"></a>Volume_relief<span class="sc">$</span>reliefVolume <span class="ot">&lt;-</span> Volume_relief<span class="sc">$</span>Value</span>
<span id="cb56-6"><a href="#cb56-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-7"><a href="#cb56-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-8"><a href="#cb56-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-9"><a href="#cb56-9" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb56-10"><a href="#cb56-10" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_preparedness, Volume_relief, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb56-11"><a href="#cb56-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-12"><a href="#cb56-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-13"><a href="#cb56-13" aria-hidden="true" tabindex="-1"></a>ww_overview2<span class="ot">&lt;-</span>Volume_total <span class="sc">%&gt;%</span></span>
<span id="cb56-14"><a href="#cb56-14" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,preparednessVolume,reliefVolume)<span class="sc">%&gt;%</span></span>
<span id="cb56-15"><a href="#cb56-15" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb56-16"><a href="#cb56-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb56-17"><a href="#cb56-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable)) <span class="sc">+</span></span>
<span id="cb56-18"><a href="#cb56-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb56-19"><a href="#cb56-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb56-20"><a href="#cb56-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb56-21"><a href="#cb56-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb56-22"><a href="#cb56-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb56-23"><a href="#cb56-23" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb56-24"><a href="#cb56-24" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb56-25"><a href="#cb56-25" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb56-26"><a href="#cb56-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;disaster preparedness&quot;</span>, <span class="st">&quot;disaster relief&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb56-27"><a href="#cb56-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, world-wide&quot;</span>)</span>
<span id="cb56-28"><a href="#cb56-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-29"><a href="#cb56-29" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-30"><a href="#cb56-30" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb56-31"><a href="#cb56-31" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb56-32"><a href="#cb56-32" aria-hidden="true" tabindex="-1"></a><span class="co"># Malawi MI</span></span>
<span id="cb56-33"><a href="#cb56-33" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb56-34"><a href="#cb56-34" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb56-35"><a href="#cb56-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-36"><a href="#cb56-36" aria-hidden="true" tabindex="-1"></a><span class="do">###  preparedness</span></span>
<span id="cb56-37"><a href="#cb56-37" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;disaster preparedness&#39; SourceCountry:MI&quot;</span></span>
<span id="cb56-38"><a href="#cb56-38" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb56-39"><a href="#cb56-39" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb56-40"><a href="#cb56-40" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=TimelineVol&amp;startdatetime=&quot;</span></span>
<span id="cb56-41"><a href="#cb56-41" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb56-42"><a href="#cb56-42" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb56-43"><a href="#cb56-43" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb56-44"><a href="#cb56-44" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb56-45"><a href="#cb56-45" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;disaster%20preparedness&#39;%20SourceCountry:MI&amp;mode=TimelineVol&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb58"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb58-2"><a href="#cb58-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb58-3"><a href="#cb58-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb58-4"><a href="#cb58-4" aria-hidden="true" tabindex="-1"></a>Volume_preparedness <span class="ot">&lt;-</span> Volume</span>
<span id="cb58-5"><a href="#cb58-5" aria-hidden="true" tabindex="-1"></a>Volume_preparedness<span class="sc">$</span>preparednessVolume <span class="ot">&lt;-</span> Volume_preparedness<span class="sc">$</span>Value</span>
<span id="cb58-6"><a href="#cb58-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb58-7"><a href="#cb58-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb58-8"><a href="#cb58-8" aria-hidden="true" tabindex="-1"></a><span class="do">###  relief</span></span>
<span id="cb58-9"><a href="#cb58-9" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;disaster relief&#39; SourceCountry:MI&quot;</span></span>
<span id="cb58-10"><a href="#cb58-10" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb58-11"><a href="#cb58-11" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb58-12"><a href="#cb58-12" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=TimelineVol&amp;startdatetime=&quot;</span></span>
<span id="cb58-13"><a href="#cb58-13" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb58-14"><a href="#cb58-14" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb58-15"><a href="#cb58-15" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb58-16"><a href="#cb58-16" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb58-17"><a href="#cb58-17" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;disaster%20relief&#39;%20SourceCountry:MI&amp;mode=TimelineVol&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb60"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb60-2"><a href="#cb60-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb60-3"><a href="#cb60-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb60-4"><a href="#cb60-4" aria-hidden="true" tabindex="-1"></a>Volume_relief <span class="ot">&lt;-</span> Volume</span>
<span id="cb60-5"><a href="#cb60-5" aria-hidden="true" tabindex="-1"></a>Volume_relief<span class="sc">$</span>reliefVolume <span class="ot">&lt;-</span> Volume_relief<span class="sc">$</span>Value</span>
<span id="cb60-6"><a href="#cb60-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb60-7"><a href="#cb60-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb60-8"><a href="#cb60-8" aria-hidden="true" tabindex="-1"></a><span class="do">### natural disaster </span></span>
<span id="cb60-9"><a href="#cb60-9" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;natural disaster&#39; SourceCountry:MI&quot;</span></span>
<span id="cb60-10"><a href="#cb60-10" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb60-11"><a href="#cb60-11" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb60-12"><a href="#cb60-12" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb60-13"><a href="#cb60-13" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb60-14"><a href="#cb60-14" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb60-15"><a href="#cb60-15" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb60-16"><a href="#cb60-16" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb60-17"><a href="#cb60-17" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;natural%20disaster&#39;%20SourceCountry:MI&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb62"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb62-1"><a href="#cb62-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb62-2"><a href="#cb62-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-3"><a href="#cb62-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb62-4"><a href="#cb62-4" aria-hidden="true" tabindex="-1"></a>Volume_disaster <span class="ot">&lt;-</span> Volume</span>
<span id="cb62-5"><a href="#cb62-5" aria-hidden="true" tabindex="-1"></a>Volume_disaster<span class="sc">$</span>disasterVolume <span class="ot">&lt;-</span> Volume_disaster<span class="sc">$</span>Value</span>
<span id="cb62-6"><a href="#cb62-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-7"><a href="#cb62-7" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb62-8"><a href="#cb62-8" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_preparedness, Volume_relief, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb62-9"><a href="#cb62-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-10"><a href="#cb62-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-11"><a href="#cb62-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-12"><a href="#cb62-12" aria-hidden="true" tabindex="-1"></a>Volume_test <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_total, Volume_disaster, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb62-13"><a href="#cb62-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-14"><a href="#cb62-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-15"><a href="#cb62-15" aria-hidden="true" tabindex="-1"></a>mi_overview2<span class="ot">&lt;-</span>Volume_test <span class="sc">%&gt;%</span></span>
<span id="cb62-16"><a href="#cb62-16" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,preparednessVolume,reliefVolume)<span class="sc">%&gt;%</span></span>
<span id="cb62-17"><a href="#cb62-17" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb62-18"><a href="#cb62-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb62-19"><a href="#cb62-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable,<span class="at">alpha=</span><span class="fl">0.3</span>)) <span class="sc">+</span></span>
<span id="cb62-20"><a href="#cb62-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb62-21"><a href="#cb62-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb62-22"><a href="#cb62-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb62-23"><a href="#cb62-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb62-24"><a href="#cb62-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb62-25"><a href="#cb62-25" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb62-26"><a href="#cb62-26" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb62-27"><a href="#cb62-27" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb62-28"><a href="#cb62-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;disaster preparedness&quot;</span>, <span class="st">&quot;disaster relief&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb62-29"><a href="#cb62-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, Malawi&quot;</span>)</span>
<span id="cb62-30"><a href="#cb62-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-31"><a href="#cb62-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-32"><a href="#cb62-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-33"><a href="#cb62-33" aria-hidden="true" tabindex="-1"></a>news_pre_rel2<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(ww_overview2,mi_overview2,</span>
<span id="cb62-34"><a href="#cb62-34" aria-hidden="true" tabindex="-1"></a>                         <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>,<span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb62-35"><a href="#cb62-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-36"><a href="#cb62-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-37"><a href="#cb62-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-38"><a href="#cb62-38" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb62-39"><a href="#cb62-39" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb62-40"><a href="#cb62-40" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb62-41"><a href="#cb62-41" aria-hidden="true" tabindex="-1"></a><span class="co"># 3. Benchmark </span></span>
<span id="cb62-42"><a href="#cb62-42" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb62-43"><a href="#cb62-43" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb62-44"><a href="#cb62-44" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb62-45"><a href="#cb62-45" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb62-46"><a href="#cb62-46" aria-hidden="true" tabindex="-1"></a><span class="do">### taxation</span></span>
<span id="cb62-47"><a href="#cb62-47" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;taxation&#39;&quot;</span></span>
<span id="cb62-48"><a href="#cb62-48" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb62-49"><a href="#cb62-49" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb62-50"><a href="#cb62-50" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb62-51"><a href="#cb62-51" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb62-52"><a href="#cb62-52" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb62-53"><a href="#cb62-53" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb62-54"><a href="#cb62-54" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb62-55"><a href="#cb62-55" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;taxation&#39;&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb64"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb64-1"><a href="#cb64-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb64-2"><a href="#cb64-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb64-3"><a href="#cb64-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb64-4"><a href="#cb64-4" aria-hidden="true" tabindex="-1"></a>Volume_taxation <span class="ot">&lt;-</span> Volume</span>
<span id="cb64-5"><a href="#cb64-5" aria-hidden="true" tabindex="-1"></a>Volume_taxation<span class="sc">$</span>taxationVolume <span class="ot">&lt;-</span> Volume_taxation<span class="sc">$</span>Value</span>
<span id="cb64-6"><a href="#cb64-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb64-7"><a href="#cb64-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb64-8"><a href="#cb64-8" aria-hidden="true" tabindex="-1"></a><span class="do">###  unemployment</span></span>
<span id="cb64-9"><a href="#cb64-9" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;unemployment&#39;&quot;</span></span>
<span id="cb64-10"><a href="#cb64-10" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb64-11"><a href="#cb64-11" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb64-12"><a href="#cb64-12" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb64-13"><a href="#cb64-13" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb64-14"><a href="#cb64-14" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb64-15"><a href="#cb64-15" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb64-16"><a href="#cb64-16" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb64-17"><a href="#cb64-17" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;unemployment&#39;&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb66"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb66-1"><a href="#cb66-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb66-2"><a href="#cb66-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-3"><a href="#cb66-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb66-4"><a href="#cb66-4" aria-hidden="true" tabindex="-1"></a>Volume_unemployment <span class="ot">&lt;-</span> Volume</span>
<span id="cb66-5"><a href="#cb66-5" aria-hidden="true" tabindex="-1"></a>Volume_unemployment<span class="sc">$</span>unemploymentVolume <span class="ot">&lt;-</span> Volume_unemployment<span class="sc">$</span>Value</span>
<span id="cb66-6"><a href="#cb66-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-7"><a href="#cb66-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-8"><a href="#cb66-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-9"><a href="#cb66-9" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb66-10"><a href="#cb66-10" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_taxation, Volume_unemployment, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb66-11"><a href="#cb66-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-12"><a href="#cb66-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-13"><a href="#cb66-13" aria-hidden="true" tabindex="-1"></a>ww_overview<span class="ot">&lt;-</span>Volume_total <span class="sc">%&gt;%</span></span>
<span id="cb66-14"><a href="#cb66-14" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,taxationVolume,unemploymentVolume)<span class="sc">%&gt;%</span></span>
<span id="cb66-15"><a href="#cb66-15" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb66-16"><a href="#cb66-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb66-17"><a href="#cb66-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable)) <span class="sc">+</span></span>
<span id="cb66-18"><a href="#cb66-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb66-19"><a href="#cb66-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb66-20"><a href="#cb66-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb66-21"><a href="#cb66-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb66-22"><a href="#cb66-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb66-23"><a href="#cb66-23" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb66-24"><a href="#cb66-24" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb66-25"><a href="#cb66-25" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb66-26"><a href="#cb66-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;taxation&quot;</span>, <span class="st">&quot;unemployment&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb66-27"><a href="#cb66-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, world-wide&quot;</span>)</span>
<span id="cb66-28"><a href="#cb66-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-29"><a href="#cb66-29" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-30"><a href="#cb66-30" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb66-31"><a href="#cb66-31" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb66-32"><a href="#cb66-32" aria-hidden="true" tabindex="-1"></a><span class="co"># Malawi MI</span></span>
<span id="cb66-33"><a href="#cb66-33" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb66-34"><a href="#cb66-34" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb66-35"><a href="#cb66-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-36"><a href="#cb66-36" aria-hidden="true" tabindex="-1"></a><span class="do">### taxation</span></span>
<span id="cb66-37"><a href="#cb66-37" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;taxation&#39; SourceCountry:MI&quot;</span></span>
<span id="cb66-38"><a href="#cb66-38" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb66-39"><a href="#cb66-39" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb66-40"><a href="#cb66-40" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb66-41"><a href="#cb66-41" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb66-42"><a href="#cb66-42" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb66-43"><a href="#cb66-43" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb66-44"><a href="#cb66-44" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb66-45"><a href="#cb66-45" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;taxation&#39;%20SourceCountry:MI&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb68"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb68-1"><a href="#cb68-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb68-2"><a href="#cb68-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb68-3"><a href="#cb68-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb68-4"><a href="#cb68-4" aria-hidden="true" tabindex="-1"></a>Volume_taxation <span class="ot">&lt;-</span> Volume</span>
<span id="cb68-5"><a href="#cb68-5" aria-hidden="true" tabindex="-1"></a>Volume_taxation<span class="sc">$</span>taxationVolume <span class="ot">&lt;-</span> Volume_taxation<span class="sc">$</span>Value</span>
<span id="cb68-6"><a href="#cb68-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb68-7"><a href="#cb68-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb68-8"><a href="#cb68-8" aria-hidden="true" tabindex="-1"></a><span class="do">###  unemployment</span></span>
<span id="cb68-9"><a href="#cb68-9" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;unemployment&#39; SourceCountry:MI&quot;</span></span>
<span id="cb68-10"><a href="#cb68-10" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb68-11"><a href="#cb68-11" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb68-12"><a href="#cb68-12" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb68-13"><a href="#cb68-13" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb68-14"><a href="#cb68-14" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb68-15"><a href="#cb68-15" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb68-16"><a href="#cb68-16" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb68-17"><a href="#cb68-17" aria-hidden="true" tabindex="-1"></a>v_url</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>[1] &quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&#39;unemployment&#39;%20SourceCountry:MI&amp;mode=timelinevolinfo&amp;startdatetime=20170101000000&amp;enddatetime=20231231000000&amp;format=CSV&quot;</code></pre>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb70"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb70-1"><a href="#cb70-1" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb70-2"><a href="#cb70-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-3"><a href="#cb70-3" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb70-4"><a href="#cb70-4" aria-hidden="true" tabindex="-1"></a>Volume_unemployment <span class="ot">&lt;-</span> Volume</span>
<span id="cb70-5"><a href="#cb70-5" aria-hidden="true" tabindex="-1"></a>Volume_unemployment<span class="sc">$</span>unemploymentVolume <span class="ot">&lt;-</span> Volume_unemployment<span class="sc">$</span>Value</span>
<span id="cb70-6"><a href="#cb70-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-7"><a href="#cb70-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-8"><a href="#cb70-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-9"><a href="#cb70-9" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb70-10"><a href="#cb70-10" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_taxation, Volume_unemployment, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb70-11"><a href="#cb70-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-12"><a href="#cb70-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-13"><a href="#cb70-13" aria-hidden="true" tabindex="-1"></a>mi_overview<span class="ot">&lt;-</span>Volume_total <span class="sc">%&gt;%</span></span>
<span id="cb70-14"><a href="#cb70-14" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,taxationVolume,unemploymentVolume)<span class="sc">%&gt;%</span></span>
<span id="cb70-15"><a href="#cb70-15" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb70-16"><a href="#cb70-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb70-17"><a href="#cb70-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable,<span class="at">alpha=</span><span class="fl">0.3</span>)) <span class="sc">+</span></span>
<span id="cb70-18"><a href="#cb70-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb70-19"><a href="#cb70-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb70-20"><a href="#cb70-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb70-21"><a href="#cb70-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb70-22"><a href="#cb70-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb70-23"><a href="#cb70-23" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb70-24"><a href="#cb70-24" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb70-25"><a href="#cb70-25" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb70-26"><a href="#cb70-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;taxation&quot;</span>, <span class="st">&quot;unemployment&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb70-27"><a href="#cb70-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, Malawi&quot;</span>)</span>
<span id="cb70-28"><a href="#cb70-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-29"><a href="#cb70-29" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-30"><a href="#cb70-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-31"><a href="#cb70-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-32"><a href="#cb70-32" aria-hidden="true" tabindex="-1"></a>news_benchmark<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(ww_overview,mi_overview,</span>
<span id="cb70-33"><a href="#cb70-33" aria-hidden="true" tabindex="-1"></a>                      <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>,<span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb70-34"><a href="#cb70-34" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb70-35"><a href="#cb70-35" aria-hidden="true" tabindex="-1"></a>plot_media<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(news_pre_rel1,news_pre_rel2,news_benchmark,</span>
<span id="cb70-36"><a href="#cb70-36" aria-hidden="true" tabindex="-1"></a>                          <span class="at">ncol =</span> <span class="dv">1</span>, <span class="at">nrow =</span> <span class="dv">3</span>)</span>
<span id="cb70-37"><a href="#cb70-37" aria-hidden="true" tabindex="-1"></a>plot_media</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<p><img 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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb71"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb71-1"><a href="#cb71-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A18_media.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">12</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="figure-a19-malawi-usage-frequency-of-various-media" class="level2">
<h2 class="anchored" data-anchor-id="figure-a19-malawi-usage-frequency-of-various-media">Figure A19: Malawi: Usage Frequency of Various Media</h2>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb72"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb72-1"><a href="#cb72-1" aria-hidden="true" tabindex="-1"></a><span class="co"># load afro-barometer 8 covariates</span></span>
<span id="cb72-2"><a href="#cb72-2" aria-hidden="true" tabindex="-1"></a>afro_8 <span class="ot">&lt;-</span> <span class="fu">read.spss</span>(<span class="st">&quot;1_input/afrobarometer_release-dataset_merge-34ctry_r8_en_2023-03-01.sav&quot;</span>, <span class="at">to.data.frame=</span><span class="cn">TRUE</span>)  <span class="sc">%&gt;%</span></span>
<span id="cb72-3"><a href="#cb72-3" aria-hidden="true" tabindex="-1"></a>   dplyr<span class="sc">::</span><span class="fu">filter</span>(COUNTRY<span class="sc">==</span><span class="st">&quot;Malawi&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb72-4"><a href="#cb72-4" aria-hidden="true" tabindex="-1"></a>  <span class="co"># select variables about news consumption</span></span>
<span id="cb72-5"><a href="#cb72-5" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(COUNTRY,RESPNO,Q55A,Q55B,Q55C,Q55D,Q55E)  <span class="sc">%&gt;%</span></span>
<span id="cb72-6"><a href="#cb72-6" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">radio =</span> Q55A,</span>
<span id="cb72-7"><a href="#cb72-7" aria-hidden="true" tabindex="-1"></a>                <span class="at">television =</span> Q55B,</span>
<span id="cb72-8"><a href="#cb72-8" aria-hidden="true" tabindex="-1"></a>                <span class="at">newspapers =</span> Q55C,</span>
<span id="cb72-9"><a href="#cb72-9" aria-hidden="true" tabindex="-1"></a>                <span class="at">internet =</span> Q55D,</span>
<span id="cb72-10"><a href="#cb72-10" aria-hidden="true" tabindex="-1"></a>                <span class="at">social_media  =</span> Q55E) </span>
<span id="cb72-11"><a href="#cb72-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb72-12"><a href="#cb72-12" aria-hidden="true" tabindex="-1"></a><span class="co"># Now let us talk about the media and how you get information about politics and other issues. 55. How often do you get news from the following sources? </span></span>
<span id="cb72-13"><a href="#cb72-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb72-14"><a href="#cb72-14" aria-hidden="true" tabindex="-1"></a>long_afro_8 <span class="ot">&lt;-</span> afro_8 <span class="sc">%&gt;%</span></span>
<span id="cb72-15"><a href="#cb72-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="at">cols =</span> radio<span class="sc">:</span>social_media, <span class="at">names_to =</span> <span class="st">&quot;Media&quot;</span>, <span class="at">values_to =</span> <span class="st">&quot;Frequency&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb72-16"><a href="#cb72-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">drop_na</span>(Frequency) <span class="co"># Optionally drop NA values if any</span></span>
<span id="cb72-17"><a href="#cb72-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb72-18"><a href="#cb72-18" aria-hidden="true" tabindex="-1"></a><span class="co"># Calculate percentages</span></span>
<span id="cb72-19"><a href="#cb72-19" aria-hidden="true" tabindex="-1"></a>percent_data <span class="ot">&lt;-</span> long_afro_8 <span class="sc">%&gt;%</span></span>
<span id="cb72-20"><a href="#cb72-20" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">group_by</span>(Media, Frequency) <span class="sc">%&gt;%</span></span>
<span id="cb72-21"><a href="#cb72-21" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">summarise</span>(<span class="at">Count =</span> dplyr<span class="sc">::</span><span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">&#39;drop&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb72-22"><a href="#cb72-22" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">group_by</span>(Media) <span class="sc">%&gt;%</span></span>
<span id="cb72-23"><a href="#cb72-23" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">Percentage =</span> Count <span class="sc">/</span> <span class="fu">sum</span>(Count) <span class="sc">*</span> <span class="dv">100</span>)</span>
<span id="cb72-24"><a href="#cb72-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb72-25"><a href="#cb72-25" aria-hidden="true" tabindex="-1"></a>fig_malawi_media<span class="ot">&lt;-</span><span class="fu">ggplot</span>(percent_data, <span class="fu">aes</span>(<span class="at">x =</span> Frequency, <span class="at">y =</span> Percentage, <span class="at">fill =</span> Frequency)) <span class="sc">+</span></span>
<span id="cb72-26"><a href="#cb72-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>) <span class="sc">+</span></span>
<span id="cb72-27"><a href="#cb72-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> Media, <span class="at">scales =</span> <span class="st">&quot;free_x&quot;</span>) <span class="sc">+</span></span>
<span id="cb72-28"><a href="#cb72-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">18</span>) <span class="sc">+</span></span>
<span id="cb72-29"><a href="#cb72-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;&quot;</span>, <span class="at">y =</span> <span class="st">&quot;Percentage&quot;</span>, <span class="at">title =</span> <span class="st">&quot;Malawi: Usage Frequency of Various Media&quot;</span>) <span class="sc">+</span></span>
<span id="cb72-30"><a href="#cb72-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb72-31"><a href="#cb72-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;none&quot;</span>)</span>
<span id="cb72-32"><a href="#cb72-32" aria-hidden="true" tabindex="-1"></a>fig_malawi_media</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<p><img 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" class="img-fluid" width="672"></p>
</div>
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb73"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A19_malawi_media.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">14</span>, <span class="at">height =</span> <span class="dv">9</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
<p>Figure A20: EU Speeches: Relative importance of topics between 2007 to 2015</p>
<div class="cell">
<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb74"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb74-1"><a href="#cb74-1" aria-hidden="true" tabindex="-1"></a><span class="do">#######################################</span></span>
<span id="cb74-2"><a href="#cb74-2" aria-hidden="true" tabindex="-1"></a><span class="co"># EU speeches</span></span>
<span id="cb74-3"><a href="#cb74-3" aria-hidden="true" tabindex="-1"></a><span class="do">#######################################</span></span>
<span id="cb74-4"><a href="#cb74-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-5"><a href="#cb74-5" aria-hidden="true" tabindex="-1"></a><span class="fu">load</span>(<span class="st">&quot;1_input/euspeech.korpus.RData&quot;</span>)</span>
<span id="cb74-6"><a href="#cb74-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-7"><a href="#cb74-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-8"><a href="#cb74-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Applying the Policy-Agendas und Laver-Garry dictionaries</span></span>
<span id="cb74-9"><a href="#cb74-9" aria-hidden="true" tabindex="-1"></a><span class="fu">load</span>(<span class="st">&quot;1_input/policy_agendas_english.RData&quot;</span>)</span>
<span id="cb74-10"><a href="#cb74-10" aria-hidden="true" tabindex="-1"></a>policyagendas.lexicon <span class="ot">&lt;-</span> <span class="fu">dictionary</span>(dictLexic2Topics)</span>
<span id="cb74-11"><a href="#cb74-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-12"><a href="#cb74-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-13"><a href="#cb74-13" aria-hidden="true" tabindex="-1"></a>dfm.eu.pa <span class="ot">&lt;-</span> <span class="fu">dfm</span>(korpus.euspeech, <span class="at">dfm_group =</span> <span class="st">&quot;country&quot;</span>, <span class="at">dictionary =</span> policyagendas.lexicon)</span>
<span id="cb74-14"><a href="#cb74-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-15"><a href="#cb74-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-16"><a href="#cb74-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-17"><a href="#cb74-17" aria-hidden="true" tabindex="-1"></a><span class="co"># Adding new categories with example keywords</span></span>
<span id="cb74-18"><a href="#cb74-18" aria-hidden="true" tabindex="-1"></a>dictLexic2Topics<span class="sc">$</span>disaster_preparedness <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;preparedness&quot;</span>,<span class="st">&quot;prevention&quot;</span>,<span class="st">&quot;disaster preparedness&quot;</span>,<span class="st">&quot;disaster prevention&quot;</span>,<span class="st">&quot;emergency preparedness&quot;</span>, <span class="st">&quot;emergency plan&quot;</span>, <span class="st">&quot;disaster plan&quot;</span>, <span class="st">&quot;evacuation plan&quot;</span>, <span class="st">&quot;crisis management&quot;</span>, <span class="st">&quot;early warning system&quot;</span>, <span class="st">&quot;emergency kit&quot;</span>, <span class="st">&quot;emergency supplies&quot;</span>, <span class="st">&quot;disaster risk reduction&quot;</span>, <span class="st">&quot;resilience building&quot;</span>, <span class="st">&quot;community preparedness&quot;</span>, <span class="st">&quot;disaster preparedness training&quot;</span>, <span class="st">&quot;hazard mitigation&quot;</span>, <span class="st">&quot;emergency response plan&quot;</span>, <span class="st">&quot;preparedness exercises&quot;</span>, <span class="st">&quot;business continuity planning&quot;</span>, <span class="st">&quot;disaster resilience&quot;</span>, <span class="st">&quot;emergency shelter&quot;</span>, <span class="st">&quot;food storage&quot;</span>, <span class="st">&quot;water purification&quot;</span>, <span class="st">&quot;first aid training&quot;</span>, <span class="st">&quot;public health preparedness&quot;</span>, <span class="st">&quot;disaster simulation&quot;</span>, <span class="st">&quot;evacuation route&quot;</span>, <span class="st">&quot;emergency communication system&quot;</span>)</span>
<span id="cb74-19"><a href="#cb74-19" aria-hidden="true" tabindex="-1"></a>dictLexic2Topics<span class="sc">$</span>disaster_relief <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;relief&quot;</span>,<span class="st">&quot;relief efforts&quot;</span>, <span class="st">&quot;aid distribution&quot;</span>, <span class="st">&quot;disaster recovery&quot;</span>, <span class="st">&quot;humanitarian aid&quot;</span>, <span class="st">&quot;emergency aid&quot;</span>, <span class="st">&quot;recovery operations&quot;</span>, <span class="st">&quot;disaster assistance&quot;</span>, <span class="st">&quot;rehabilitation efforts&quot;</span>, <span class="st">&quot;relief supplies&quot;</span>, <span class="st">&quot;medical relief&quot;</span>, <span class="st">&quot;search and rescue&quot;</span>, <span class="st">&quot;emergency medical services&quot;</span>, <span class="st">&quot;food aid&quot;</span>, <span class="st">&quot;shelter provision&quot;</span>, <span class="st">&quot;post-disaster recovery&quot;</span>, <span class="st">&quot;psychosocial support&quot;</span>, <span class="st">&quot;reconstruction assistance&quot;</span>, <span class="st">&quot;debris removal&quot;</span>, <span class="st">&quot;donation management&quot;</span>, <span class="st">&quot;volunteer coordination&quot;</span>, <span class="st">&quot;crisis counseling&quot;</span>, <span class="st">&quot;financial assistance&quot;</span>, <span class="st">&quot;temporary housing&quot;</span>, <span class="st">&quot;disaster victim identification&quot;</span>, <span class="st">&quot;infrastructure repair&quot;</span>)</span>
<span id="cb74-20"><a href="#cb74-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-21"><a href="#cb74-21" aria-hidden="true" tabindex="-1"></a>policyagendas.lexicon <span class="ot">&lt;-</span> <span class="fu">dictionary</span>(dictLexic2Topics)</span>
<span id="cb74-22"><a href="#cb74-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-23"><a href="#cb74-23" aria-hidden="true" tabindex="-1"></a><span class="co"># Ensure your corpus has the necessary metadata, including the date</span></span>
<span id="cb74-24"><a href="#cb74-24" aria-hidden="true" tabindex="-1"></a><span class="co"># This step is skipped here as it&#39;s assumed your corpus is already prepared</span></span>
<span id="cb74-25"><a href="#cb74-25" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-26"><a href="#cb74-26" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a Document-Feature Matrix (DFM) using the dictionary for topic categorization</span></span>
<span id="cb74-27"><a href="#cb74-27" aria-hidden="true" tabindex="-1"></a>dfm.eu.pa <span class="ot">&lt;-</span> <span class="fu">dfm</span>(korpus.euspeech) <span class="sc">%&gt;%</span></span>
<span id="cb74-28"><a href="#cb74-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">dfm_lookup</span>(<span class="at">dictionary =</span> policyagendas.lexicon)</span>
<span id="cb74-29"><a href="#cb74-29" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-30"><a href="#cb74-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-31"><a href="#cb74-31" aria-hidden="true" tabindex="-1"></a><span class="co"># Assuming &#39;Jahr&#39; is the column in &#39;korpus.euspeech.stats&#39; with the year information</span></span>
<span id="cb74-32"><a href="#cb74-32" aria-hidden="true" tabindex="-1"></a><span class="co"># and it correctly aligns with the documents in your DFM</span></span>
<span id="cb74-33"><a href="#cb74-33" aria-hidden="true" tabindex="-1"></a><span class="fu">docvars</span>(dfm.eu.pa, <span class="st">&quot;year&quot;</span>) <span class="ot">&lt;-</span> korpus.euspeech.stats<span class="sc">$</span>Jahr</span>
<span id="cb74-34"><a href="#cb74-34" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-35"><a href="#cb74-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb74-36"><a href="#cb74-36" aria-hidden="true" tabindex="-1"></a><span class="co"># Check for any NA values in year assignment</span></span>
<span id="cb74-37"><a href="#cb74-37" aria-hidden="true" tabindex="-1"></a><span class="fu">sum</span>(<span class="fu">is.na</span>(<span class="fu">docvars</span>(dfm.eu.pa, <span class="st">&quot;year&quot;</span>)))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<pre><code>[1] 0</code></pre>
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<details open>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb76"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb76-1"><a href="#cb76-1" aria-hidden="true" tabindex="-1"></a>dfm_by_year <span class="ot">&lt;-</span> <span class="fu">dfm_group</span>(dfm.eu.pa, <span class="at">groups =</span> <span class="fu">docvars</span>(dfm.eu.pa, <span class="st">&quot;year&quot;</span>))</span>
<span id="cb76-2"><a href="#cb76-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-3"><a href="#cb76-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert counts to proportions within each year</span></span>
<span id="cb76-4"><a href="#cb76-4" aria-hidden="true" tabindex="-1"></a>dfm_prop <span class="ot">&lt;-</span> <span class="fu">dfm_weight</span>(dfm_by_year, <span class="at">scheme =</span> <span class="st">&quot;prop&quot;</span>)</span>
<span id="cb76-5"><a href="#cb76-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-6"><a href="#cb76-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-7"><a href="#cb76-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert DFM to a dense matrix, then to a data frame</span></span>
<span id="cb76-8"><a href="#cb76-8" aria-hidden="true" tabindex="-1"></a>dfm_matrix <span class="ot">&lt;-</span> <span class="fu">as.matrix</span>(dfm_prop)</span>
<span id="cb76-9"><a href="#cb76-9" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">year =</span> <span class="fu">rownames</span>(dfm_matrix), dfm_matrix)</span>
<span id="cb76-10"><a href="#cb76-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-11"><a href="#cb76-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Use pivot_longer to convert wide data to long format</span></span>
<span id="cb76-12"><a href="#cb76-12" aria-hidden="true" tabindex="-1"></a>df_long <span class="ot">&lt;-</span> <span class="fu">pivot_longer</span>(df, <span class="at">cols =</span> <span class="sc">-</span>year, <span class="at">names_to =</span> <span class="st">&quot;topic&quot;</span>, <span class="at">values_to =</span> <span class="st">&quot;frequency&quot;</span>)</span>
<span id="cb76-13"><a href="#cb76-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-14"><a href="#cb76-14" aria-hidden="true" tabindex="-1"></a><span class="co"># Note: If the year column is not character, convert it to avoid issues in ggplot</span></span>
<span id="cb76-15"><a href="#cb76-15" aria-hidden="true" tabindex="-1"></a>df_long<span class="sc">$</span>year <span class="ot">&lt;-</span> <span class="fu">as.character</span>(df_long<span class="sc">$</span>year)</span>
<span id="cb76-16"><a href="#cb76-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-17"><a href="#cb76-17" aria-hidden="true" tabindex="-1"></a>target <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;disaster_relief&quot;</span>, <span class="st">&quot;disaster_preparedness&quot;</span>)</span>
<span id="cb76-18"><a href="#cb76-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-19"><a href="#cb76-19" aria-hidden="true" tabindex="-1"></a>prep_relief<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span></span>
<span id="cb76-20"><a href="#cb76-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(frequency<span class="sc">&lt;</span><span class="fl">0.0025</span>)<span class="sc">%&gt;%</span></span>
<span id="cb76-21"><a href="#cb76-21" aria-hidden="true" tabindex="-1"></a>  <span class="co"># filter( topic %in% target)  %&gt;% # equivalently, dat %&gt;% filter(name %in% target)</span></span>
<span id="cb76-22"><a href="#cb76-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> year, <span class="at">y =</span> frequency, <span class="at">color =</span> topic, <span class="at">group =</span> topic)) <span class="sc">+</span></span>
<span id="cb76-23"><a href="#cb76-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb76-24"><a href="#cb76-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb76-25"><a href="#cb76-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Prepardness and Relief + similar importance&quot;</span>,</span>
<span id="cb76-26"><a href="#cb76-26" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Year&quot;</span>,</span>
<span id="cb76-27"><a href="#cb76-27" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Relative Importance&quot;</span>,</span>
<span id="cb76-28"><a href="#cb76-28" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Topic&quot;</span>) <span class="sc">+</span></span>
<span id="cb76-29"><a href="#cb76-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>))<span class="sc">+</span></span>
<span id="cb76-30"><a href="#cb76-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb76-31"><a href="#cb76-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-32"><a href="#cb76-32" aria-hidden="true" tabindex="-1"></a>all<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span></span>
<span id="cb76-33"><a href="#cb76-33" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(frequency<span class="sc">&gt;</span><span class="fl">0.05</span>)<span class="sc">%&gt;%</span></span>
<span id="cb76-34"><a href="#cb76-34" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> year, <span class="at">y =</span> frequency, <span class="at">color =</span> topic, <span class="at">group =</span> topic)) <span class="sc">+</span></span>
<span id="cb76-35"><a href="#cb76-35" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb76-36"><a href="#cb76-36" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb76-37"><a href="#cb76-37" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Top 5 topics&quot;</span>,</span>
<span id="cb76-38"><a href="#cb76-38" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Year&quot;</span>,</span>
<span id="cb76-39"><a href="#cb76-39" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Relative Importance&quot;</span>,</span>
<span id="cb76-40"><a href="#cb76-40" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Topic&quot;</span>) <span class="sc">+</span></span>
<span id="cb76-41"><a href="#cb76-41" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>))<span class="sc">+</span></span>
<span id="cb76-42"><a href="#cb76-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb76-43"><a href="#cb76-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-44"><a href="#cb76-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb76-45"><a href="#cb76-45" aria-hidden="true" tabindex="-1"></a>fig_eu_speech<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(prep_relief,all,</span>
<span id="cb76-46"><a href="#cb76-46" aria-hidden="true" tabindex="-1"></a>                          <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>)</span>
<span id="cb76-47"><a href="#cb76-47" aria-hidden="true" tabindex="-1"></a>fig_eu_speech</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p><img 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" 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</div>
<details open>
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<div class="sourceCode" id="cb78" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb78-1"><a href="#cb78-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb78-2"><a href="#cb78-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> &quot;Replication: Do Voters Reward Value Over Prevention? Evidence from Disaster Policy Preferences&quot;</span></span>
<span id="cb78-3"><a href="#cb78-3" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span></span>
<span id="cb78-4"><a href="#cb78-4" aria-hidden="true" tabindex="-1"></a><span class="co">  - name: Felix Hartmann</span></span>
<span id="cb78-5"><a href="#cb78-5" aria-hidden="true" tabindex="-1"></a><span class="co">    orcid: 0000-0002-6134-7126</span></span>
<span id="cb78-6"><a href="#cb78-6" aria-hidden="true" tabindex="-1"></a><span class="co">    email: feha.egb@cbs.dk</span></span>
<span id="cb78-7"><a href="#cb78-7" aria-hidden="true" tabindex="-1"></a><span class="co">    affiliations:</span></span>
<span id="cb78-8"><a href="#cb78-8" aria-hidden="true" tabindex="-1"></a><span class="co">      - name: Copenhagen Business School</span></span>
<span id="cb78-9"><a href="#cb78-9" aria-hidden="true" tabindex="-1"></a><span class="an">date:</span><span class="co"> &quot;`r format(Sys.time(), &#39;%d %B, %Y&#39;)`&quot;</span></span>
<span id="cb78-10"><a href="#cb78-10" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
<span id="cb78-11"><a href="#cb78-11" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
<span id="cb78-12"><a href="#cb78-12" aria-hidden="true" tabindex="-1"></a><span class="co">    toc: true</span></span>
<span id="cb78-13"><a href="#cb78-13" aria-hidden="true" tabindex="-1"></a><span class="co">    toc-location: left</span></span>
<span id="cb78-14"><a href="#cb78-14" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: show</span></span>
<span id="cb78-15"><a href="#cb78-15" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
<span id="cb78-16"><a href="#cb78-16" aria-hidden="true" tabindex="-1"></a><span class="co">    embed-resources: true</span></span>
<span id="cb78-17"><a href="#cb78-17" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb78-18"><a href="#cb78-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-19"><a href="#cb78-19" aria-hidden="true" tabindex="-1"></a><span class="fu"># Summary information (README)</span></span>
<span id="cb78-20"><a href="#cb78-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-21"><a href="#cb78-21" aria-hidden="true" tabindex="-1"></a><span class="in">```{r class.source = &quot;fold-show&quot;, eval = FALSE, echo = TRUE}</span></span>
<span id="cb78-22"><a href="#cb78-22" aria-hidden="true" tabindex="-1"></a><span class="sc">----------------------------</span></span>
<span id="cb78-23"><a href="#cb78-23" aria-hidden="true" tabindex="-1"></a>initial deposit date<span class="sc">:</span> <span class="dv">08</span><span class="sc">/</span><span class="dv">01</span><span class="sc">/</span><span class="dv">2025</span></span>
<span id="cb78-24"><a href="#cb78-24" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb78-25"><a href="#cb78-25" aria-hidden="true" tabindex="-1"></a>Replication code and data needed to reproduce results presented <span class="cf">in</span><span class="sc">-</span>text and <span class="cf">in</span> the Supplemental Information provided <span class="cf">for</span> the study. </span>
<span id="cb78-26"><a href="#cb78-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-27"><a href="#cb78-27" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb78-28"><a href="#cb78-28" aria-hidden="true" tabindex="-1"></a><span class="co"># code overview</span></span>
<span id="cb78-29"><a href="#cb78-29" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;0_replication.qmd&quot;</span> R Quarto script produces all the numeric results, tables, and figures <span class="cf">in</span> the main text and <span class="cf">in</span> the supplementary information</span>
<span id="cb78-30"><a href="#cb78-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-31"><a href="#cb78-31" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb78-32"><a href="#cb78-32" aria-hidden="true" tabindex="-1"></a><span class="co"># files:</span></span>
<span id="cb78-33"><a href="#cb78-33" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input&quot;</span> contains all data needed <span class="cf">for</span> replication</span>
<span id="cb78-34"><a href="#cb78-34" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;2_output&quot;</span> contains numeric results, tables, and figures </span>
<span id="cb78-35"><a href="#cb78-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-36"><a href="#cb78-36" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb78-37"><a href="#cb78-37" aria-hidden="true" tabindex="-1"></a><span class="co"># 1_input:</span></span>
<span id="cb78-38"><a href="#cb78-38" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/afrobarometer_release-dataset_merge-34ctry_r8_en_2023-03-01.sav&quot;</span></span>
<span id="cb78-39"><a href="#cb78-39" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> Afrobarometer Merged Round <span class="dv">8</span> <span class="fu">data</span> (<span class="dv">34</span> countries) (<span class="dv">2022</span>)</span>
<span id="cb78-40"><a href="#cb78-40" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> source<span class="sc">:</span> https<span class="sc">:</span><span class="er">//</span>www.afrobarometer.org<span class="sc">/</span>data<span class="sc">/</span>merged<span class="sc">-</span>data<span class="sc">/</span></span>
<span id="cb78-41"><a href="#cb78-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-42"><a href="#cb78-42" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/emdat_public_2020_12_11_query_uid-GZ254X.xlsx&quot;</span></span>
<span id="cb78-43"><a href="#cb78-43" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> EM<span class="sc">-</span>DAT<span class="sc">:</span> The CRED<span class="sc">/</span>OFDA International Disaster Database</span>
<span id="cb78-44"><a href="#cb78-44" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> source<span class="sc">:</span> https<span class="sc">:</span><span class="er">//</span>public.emdat.be<span class="sc">/</span> </span>
<span id="cb78-45"><a href="#cb78-45" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-46"><a href="#cb78-46" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/ND_Gain_id_infr_06_raw0.csv&quot;</span></span>
<span id="cb78-47"><a href="#cb78-47" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> Notre Dame Global Adaptation Initiative Country <span class="fu">Index</span> (ND<span class="sc">-</span>GAIN)</span>
<span id="cb78-48"><a href="#cb78-48" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> variable<span class="sc">:</span> <span class="fu">ID_INFR_06</span> (disaster preparedness)</span>
<span id="cb78-49"><a href="#cb78-49" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> source<span class="sc">:</span> https<span class="sc">:</span><span class="er">//</span>gain.nd.edu<span class="sc">/</span>our<span class="sc">-</span>work<span class="sc">/</span>country<span class="sc">-</span>index<span class="sc">/</span>download<span class="sc">-</span>data<span class="sc">/</span></span>
<span id="cb78-50"><a href="#cb78-50" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-51"><a href="#cb78-51" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/custom.geo.json&quot;</span></span>
<span id="cb78-52"><a href="#cb78-52" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> Africa shapefile</span>
<span id="cb78-53"><a href="#cb78-53" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-54"><a href="#cb78-54" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/conjoint.rds&quot;</span> </span>
<span id="cb78-55"><a href="#cb78-55" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> contains all data from the conjoint experiment </span>
<span id="cb78-56"><a href="#cb78-56" aria-hidden="true" tabindex="-1"></a>    <span class="sc">+</span> long format </span>
<span id="cb78-57"><a href="#cb78-57" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/covariates.rds&quot;</span></span>
<span id="cb78-58"><a href="#cb78-58" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> contains all covariates  </span>
<span id="cb78-59"><a href="#cb78-59" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> merge by case id  to conjoint data</span>
<span id="cb78-60"><a href="#cb78-60" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/euspeech.korpus.RData&quot;</span> </span>
<span id="cb78-61"><a href="#cb78-61" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> EUSpeech <span class="fu">dataset</span> (Schumacher et al., <span class="dv">2016</span>)</span>
<span id="cb78-62"><a href="#cb78-62" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> <span class="st">&quot;1_input/policy_agendas_english.RData&quot;</span> </span>
<span id="cb78-63"><a href="#cb78-63" aria-hidden="true" tabindex="-1"></a>  <span class="sc">+</span> source<span class="sc">:</span> https<span class="sc">:</span><span class="er">//</span>github.com<span class="sc">/</span>cbpuschmann<span class="sc">/</span>quanteda_mzes<span class="sc">/</span>tree<span class="sc">/</span>master </span>
<span id="cb78-64"><a href="#cb78-64" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-65"><a href="#cb78-65" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-66"><a href="#cb78-66" aria-hidden="true" tabindex="-1"></a><span class="sc">-----------------------------</span></span>
<span id="cb78-67"><a href="#cb78-67" aria-hidden="true" tabindex="-1"></a><span class="co"># running time </span></span>
<span id="cb78-68"><a href="#cb78-68" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span> For <span class="st">&quot;0_replication.qmd&quot;</span>, about <span class="dv">5</span> min <span class="cf">in</span> total</span>
<span id="cb78-69"><a href="#cb78-69" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-70"><a href="#cb78-70" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-71"><a href="#cb78-71" aria-hidden="true" tabindex="-1"></a><span class="fu"># Housekeeping</span></span>
<span id="cb78-72"><a href="#cb78-72" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-73"><a href="#cb78-73" aria-hidden="true" tabindex="-1"></a><span class="fu">## load packages</span></span>
<span id="cb78-74"><a href="#cb78-74" aria-hidden="true" tabindex="-1"></a><span class="in">```{r setup, echo=TRUE}</span></span>
<span id="cb78-75"><a href="#cb78-75" aria-hidden="true" tabindex="-1"></a><span class="fu">rm</span>(<span class="at">list=</span><span class="fu">ls</span>())  </span>
<span id="cb78-76"><a href="#cb78-76" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-77"><a href="#cb78-77" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">modelsummary_format_numeric_latex =</span> <span class="st">&quot;mathmode&quot;</span>)</span>
<span id="cb78-78"><a href="#cb78-78" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-79"><a href="#cb78-79" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span>opts_chunk<span class="sc">$</span><span class="fu">set</span>(<span class="at">echo =</span> <span class="cn">TRUE</span>)</span>
<span id="cb78-80"><a href="#cb78-80" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-81"><a href="#cb78-81" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">require</span>(pacman)) <span class="fu">install.packages</span>(<span class="st">&quot;pacman&quot;</span>)</span>
<span id="cb78-82"><a href="#cb78-82" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(</span>
<span id="cb78-83"><a href="#cb78-83" aria-hidden="true" tabindex="-1"></a>  car,   <span class="co"># linear hypothesis check </span></span>
<span id="cb78-84"><a href="#cb78-84" aria-hidden="true" tabindex="-1"></a>  dplyr,</span>
<span id="cb78-85"><a href="#cb78-85" aria-hidden="true" tabindex="-1"></a>  tidyverse,</span>
<span id="cb78-86"><a href="#cb78-86" aria-hidden="true" tabindex="-1"></a>  estimatr,</span>
<span id="cb78-87"><a href="#cb78-87" aria-hidden="true" tabindex="-1"></a>  here,</span>
<span id="cb78-88"><a href="#cb78-88" aria-hidden="true" tabindex="-1"></a>  reshape,</span>
<span id="cb78-89"><a href="#cb78-89" aria-hidden="true" tabindex="-1"></a>  quanteda, <span class="co"># speech data</span></span>
<span id="cb78-90"><a href="#cb78-90" aria-hidden="true" tabindex="-1"></a>  readtext, <span class="co"># speech data</span></span>
<span id="cb78-91"><a href="#cb78-91" aria-hidden="true" tabindex="-1"></a>  lubridate,<span class="co"># speech data</span></span>
<span id="cb78-92"><a href="#cb78-92" aria-hidden="true" tabindex="-1"></a>  cregg, <span class="co"># subgroup analysis </span></span>
<span id="cb78-93"><a href="#cb78-93" aria-hidden="true" tabindex="-1"></a>  compare,</span>
<span id="cb78-94"><a href="#cb78-94" aria-hidden="true" tabindex="-1"></a>  modelsummary,</span>
<span id="cb78-95"><a href="#cb78-95" aria-hidden="true" tabindex="-1"></a>  texreg,</span>
<span id="cb78-96"><a href="#cb78-96" aria-hidden="true" tabindex="-1"></a>  gridExtra,</span>
<span id="cb78-97"><a href="#cb78-97" aria-hidden="true" tabindex="-1"></a>  grid,</span>
<span id="cb78-98"><a href="#cb78-98" aria-hidden="true" tabindex="-1"></a>  ggplot2,</span>
<span id="cb78-99"><a href="#cb78-99" aria-hidden="true" tabindex="-1"></a>  lattice,</span>
<span id="cb78-100"><a href="#cb78-100" aria-hidden="true" tabindex="-1"></a>  vtable,   <span class="co"># Summary Stats</span></span>
<span id="cb78-101"><a href="#cb78-101" aria-hidden="true" tabindex="-1"></a>  broom,   <span class="co"># Summary Stats</span></span>
<span id="cb78-102"><a href="#cb78-102" aria-hidden="true" tabindex="-1"></a>  broom.helpers,</span>
<span id="cb78-103"><a href="#cb78-103" aria-hidden="true" tabindex="-1"></a>  lubridate,</span>
<span id="cb78-104"><a href="#cb78-104" aria-hidden="true" tabindex="-1"></a>  fBasics,</span>
<span id="cb78-105"><a href="#cb78-105" aria-hidden="true" tabindex="-1"></a>  readr,</span>
<span id="cb78-106"><a href="#cb78-106" aria-hidden="true" tabindex="-1"></a>  ggpubr,</span>
<span id="cb78-107"><a href="#cb78-107" aria-hidden="true" tabindex="-1"></a>  foreign,</span>
<span id="cb78-108"><a href="#cb78-108" aria-hidden="true" tabindex="-1"></a>  readxl, <span class="co"># read excel files</span></span>
<span id="cb78-109"><a href="#cb78-109" aria-hidden="true" tabindex="-1"></a>  viridisLite, <span class="co"># colour scheme</span></span>
<span id="cb78-110"><a href="#cb78-110" aria-hidden="true" tabindex="-1"></a>  viridis,<span class="co"># colour scheme</span></span>
<span id="cb78-111"><a href="#cb78-111" aria-hidden="true" tabindex="-1"></a>  sf, <span class="co">#  spatial data</span></span>
<span id="cb78-112"><a href="#cb78-112" aria-hidden="true" tabindex="-1"></a>  ggthemes </span>
<span id="cb78-113"><a href="#cb78-113" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-114"><a href="#cb78-114" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-115"><a href="#cb78-115" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-116"><a href="#cb78-116" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-117"><a href="#cb78-117" aria-hidden="true" tabindex="-1"></a><span class="fu">## load function</span></span>
<span id="cb78-120"><a href="#cb78-120" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb78-121"><a href="#cb78-121" aria-hidden="true" tabindex="-1"></a><span class="do">######################################</span></span>
<span id="cb78-122"><a href="#cb78-122" aria-hidden="true" tabindex="-1"></a><span class="co"># functions</span></span>
<span id="cb78-123"><a href="#cb78-123" aria-hidden="true" tabindex="-1"></a><span class="do">######################################</span></span>
<span id="cb78-124"><a href="#cb78-124" aria-hidden="true" tabindex="-1"></a>tidy_coefs_with_ref <span class="ot">&lt;-</span> <span class="cf">function</span>(mod_obj, <span class="at">sep =</span> <span class="st">&quot;_&quot;</span>){</span>
<span id="cb78-125"><a href="#cb78-125" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-126"><a href="#cb78-126" aria-hidden="true" tabindex="-1"></a>  tidy_coefs <span class="ot">&lt;-</span> <span class="fu">tidy</span>(mod_obj) <span class="sc">%&gt;%</span> </span>
<span id="cb78-127"><a href="#cb78-127" aria-hidden="true" tabindex="-1"></a>    <span class="fu">separate</span>(term, <span class="fu">c</span>(<span class="st">&quot;variable&quot;</span>, <span class="st">&quot;level&quot;</span>), sep, <span class="at">remove =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb78-128"><a href="#cb78-128" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">variable =</span> <span class="fu">paste0</span>(variable, sep))</span>
<span id="cb78-129"><a href="#cb78-129" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-130"><a href="#cb78-130" aria-hidden="true" tabindex="-1"></a>  xlevels <span class="ot">&lt;-</span> mod_obj<span class="sc">$</span>xlevels  </span>
<span id="cb78-131"><a href="#cb78-131" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-132"><a href="#cb78-132" aria-hidden="true" tabindex="-1"></a>  missing_levels <span class="ot">&lt;-</span> xlevels <span class="sc">%&gt;%</span> </span>
<span id="cb78-133"><a href="#cb78-133" aria-hidden="true" tabindex="-1"></a>    <span class="fu">enframe</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb78-134"><a href="#cb78-134" aria-hidden="true" tabindex="-1"></a>    <span class="fu">unnest</span>(<span class="at">cols =</span> <span class="fu">c</span>(value)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-135"><a href="#cb78-135" aria-hidden="true" tabindex="-1"></a>    <span class="fu">set_names</span>(<span class="fu">c</span>(<span class="st">&quot;variable&quot;</span>, <span class="st">&quot;level&quot;</span>))</span>
<span id="cb78-136"><a href="#cb78-136" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-137"><a href="#cb78-137" aria-hidden="true" tabindex="-1"></a>  missing_levels <span class="sc">%&gt;%</span> </span>
<span id="cb78-138"><a href="#cb78-138" aria-hidden="true" tabindex="-1"></a>    <span class="fu">anti_join</span>(tidy_coefs) <span class="sc">%&gt;%</span> </span>
<span id="cb78-139"><a href="#cb78-139" aria-hidden="true" tabindex="-1"></a>    <span class="fu">bind_rows</span>(tidy_coefs) </span>
<span id="cb78-140"><a href="#cb78-140" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb78-141"><a href="#cb78-141" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-142"><a href="#cb78-142" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-143"><a href="#cb78-143" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-144"><a href="#cb78-144" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-145"><a href="#cb78-145" aria-hidden="true" tabindex="-1"></a><span class="fu">## Session Info</span></span>
<span id="cb78-148"><a href="#cb78-148" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb78-149"><a href="#cb78-149" aria-hidden="true" tabindex="-1"></a><span class="fu">sessionInfo</span>()</span>
<span id="cb78-150"><a href="#cb78-150" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-151"><a href="#cb78-151" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-152"><a href="#cb78-152" aria-hidden="true" tabindex="-1"></a><span class="fu">## load data</span></span>
<span id="cb78-155"><a href="#cb78-155" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb78-156"><a href="#cb78-156" aria-hidden="true" tabindex="-1"></a>here<span class="sc">::</span><span class="fu">i_am</span>(<span class="st">&quot;0_replication.qmd&quot;</span>)</span>
<span id="cb78-157"><a href="#cb78-157" aria-hidden="true" tabindex="-1"></a>df_long <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;1_input/conjoint.rds&quot;</span>)</span>
<span id="cb78-158"><a href="#cb78-158" aria-hidden="true" tabindex="-1"></a>df_long<span class="ot">&lt;-</span><span class="fu">as_tibble</span>(df_long)</span>
<span id="cb78-159"><a href="#cb78-159" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-160"><a href="#cb78-160" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-161"><a href="#cb78-161" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-162"><a href="#cb78-162" aria-hidden="true" tabindex="-1"></a><span class="fu"># Main Analysis</span></span>
<span id="cb78-163"><a href="#cb78-163" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure 1: Main Results</span></span>
<span id="cb78-164"><a href="#cb78-164" aria-hidden="true" tabindex="-1"></a><span class="in">```{r,warning=FALSE}</span></span>
<span id="cb78-165"><a href="#cb78-165" aria-hidden="true" tabindex="-1"></a>df_long_main<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> dplyr<span class="sc">::</span><span class="fu">rename</span>(</span>
<span id="cb78-166"><a href="#cb78-166" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffortPrevention_ =</span> Effort_prevention,</span>
<span id="cb78-167"><a href="#cb78-167" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffortRelief_ =</span> Effort_relief,</span>
<span id="cb78-168"><a href="#cb78-168" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffectivePrevention_ =</span> Effective_prevention,</span>
<span id="cb78-169"><a href="#cb78-169" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffectiveRelief_ =</span> Effective_relief,</span>
<span id="cb78-170"><a href="#cb78-170" aria-hidden="true" tabindex="-1"></a>                          <span class="at">Ask_ =</span> Ask,</span>
<span id="cb78-171"><a href="#cb78-171" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EQ_ =</span> EQ,</span>
<span id="cb78-172"><a href="#cb78-172" aria-hidden="true" tabindex="-1"></a>                          <span class="at">Honesty_ =</span> Honesty</span>
<span id="cb78-173"><a href="#cb78-173" aria-hidden="true" tabindex="-1"></a>                          )</span>
<span id="cb78-174"><a href="#cb78-174" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-175"><a href="#cb78-175" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-176"><a href="#cb78-176" aria-hidden="true" tabindex="-1"></a>  res<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>EffortPrevention_<span class="sc">+</span>EffortRelief_<span class="sc">+</span>EffectiveRelief_<span class="sc">+</span>EffectivePrevention_<span class="sc">+</span>Ask_<span class="sc">+</span>EQ_<span class="sc">+</span>Honesty_,</span>
<span id="cb78-177"><a href="#cb78-177" aria-hidden="true" tabindex="-1"></a>                  <span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID, <span class="at">se_type =</span> <span class="st">&quot;stata&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-178"><a href="#cb78-178" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy_coefs_with_ref</span>() <span class="sc">%&gt;%</span></span>
<span id="cb78-179"><a href="#cb78-179" aria-hidden="true" tabindex="-1"></a>  <span class="co"># remove intercept</span></span>
<span id="cb78-180"><a href="#cb78-180" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="sc">!</span>(variable <span class="sc">==</span> <span class="st">&#39;(Intercept)_&#39;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-181"><a href="#cb78-181" aria-hidden="true" tabindex="-1"></a>  <span class="co"># add space for level</span></span>
<span id="cb78-182"><a href="#cb78-182" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">paste</span>(level, <span class="st">&quot;   &quot;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-183"><a href="#cb78-183" aria-hidden="true" tabindex="-1"></a>  <span class="co"># fill value</span></span>
<span id="cb78-184"><a href="#cb78-184" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">estimate =</span> tidyr<span class="sc">::</span><span class="fu">replace_na</span>(estimate, <span class="dv">0</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-185"><a href="#cb78-185" aria-hidden="true" tabindex="-1"></a>    <span class="co"># rename</span></span>
<span id="cb78-186"><a href="#cb78-186" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(    </span>
<span id="cb78-187"><a href="#cb78-187" aria-hidden="true" tabindex="-1"></a>  <span class="at">variable=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(variable, </span>
<span id="cb78-188"><a href="#cb78-188" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Ask_&quot;</span><span class="ot">=</span><span class="st">&quot;E. Ask_&quot;</span>, </span>
<span id="cb78-189"><a href="#cb78-189" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Honesty_&quot;</span> <span class="ot">=</span> <span class="st">&quot;G. Honesty_&quot;</span>,</span>
<span id="cb78-190"><a href="#cb78-190" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EQ_&quot;</span> <span class="ot">=</span> <span class="st">&quot;F. EQ_&quot;</span>,</span>
<span id="cb78-191"><a href="#cb78-191" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EffectivePrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;C. EffectivePrevention_&quot;</span>,</span>
<span id="cb78-192"><a href="#cb78-192" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EffectiveRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;D. EffectiveRelief_&quot;</span>,</span>
<span id="cb78-193"><a href="#cb78-193" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EffortPrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;A. EffortPrevention_&quot;</span>,</span>
<span id="cb78-194"><a href="#cb78-194" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EffortRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;B. EffortRelief_&quot;</span></span>
<span id="cb78-195"><a href="#cb78-195" aria-hidden="true" tabindex="-1"></a>  ))    <span class="sc">%&gt;%</span> </span>
<span id="cb78-196"><a href="#cb78-196" aria-hidden="true" tabindex="-1"></a>  <span class="co"># add empty raw</span></span>
<span id="cb78-197"><a href="#cb78-197" aria-hidden="true" tabindex="-1"></a>  <span class="fu">as_tibble</span>() <span class="sc">%&gt;%</span></span>
<span id="cb78-198"><a href="#cb78-198" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(variable) <span class="sc">%&gt;%</span> </span>
<span id="cb78-199"><a href="#cb78-199" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_modify</span>(<span class="sc">~</span> <span class="fu">add_row</span>(.x,<span class="at">.before=</span><span class="dv">0</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-200"><a href="#cb78-200" aria-hidden="true" tabindex="-1"></a>  <span class="co">#</span></span>
<span id="cb78-201"><a href="#cb78-201" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_reorder</span>(level, estimate,<span class="at">.na_rm =</span> <span class="cn">TRUE</span>))  <span class="sc">%&gt;%</span> </span>
<span id="cb78-202"><a href="#cb78-202" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> dplyr<span class="sc">::</span><span class="fu">coalesce</span>(level,variable))   <span class="sc">%&gt;%</span> </span>
<span id="cb78-203"><a href="#cb78-203" aria-hidden="true" tabindex="-1"></a>    <span class="co"># rename</span></span>
<span id="cb78-204"><a href="#cb78-204" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(    </span>
<span id="cb78-205"><a href="#cb78-205" aria-hidden="true" tabindex="-1"></a>  <span class="at">level=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(level, </span>
<span id="cb78-206"><a href="#cb78-206" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;A. EffortPrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effort Preparedness&quot;</span>,</span>
<span id="cb78-207"><a href="#cb78-207" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;B. EffortRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effort Relief&quot;</span>,</span>
<span id="cb78-208"><a href="#cb78-208" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;C. EffectivePrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effective Preparedness&quot;</span>,</span>
<span id="cb78-209"><a href="#cb78-209" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;D. EffectiveRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effective Relief&quot;</span>,</span>
<span id="cb78-210"><a href="#cb78-210" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;E. Ask_&quot;</span><span class="ot">=</span><span class="st">&quot;Ask Help&quot;</span>, </span>
<span id="cb78-211"><a href="#cb78-211" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;F. EQ_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb78-212"><a href="#cb78-212" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;G. Honesty_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span></span>
<span id="cb78-213"><a href="#cb78-213" aria-hidden="true" tabindex="-1"></a>  )) <span class="sc">%&gt;%</span></span>
<span id="cb78-214"><a href="#cb78-214" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_column</span>(<span class="at">indicator =</span> <span class="dv">0</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-215"><a href="#cb78-215" aria-hidden="true" tabindex="-1"></a>  <span class="co"># replace</span></span>
<span id="cb78-216"><a href="#cb78-216" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-217"><a href="#cb78-217" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;A. EffortPrevention_&quot;</span>, <span class="st">&quot;Effort&quot;</span>),</span>
<span id="cb78-218"><a href="#cb78-218" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;B. EffortRelief_&quot;</span>, <span class="st">&quot;Effort&quot;</span>),</span>
<span id="cb78-219"><a href="#cb78-219" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;C. EffectivePrevention_&quot;</span>, <span class="st">&quot;Effective&quot;</span>),</span>
<span id="cb78-220"><a href="#cb78-220" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;D. EffectiveRelief_&quot;</span>, <span class="st">&quot;Effective&quot;</span>),</span>
<span id="cb78-221"><a href="#cb78-221" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;E. Ask_&quot;</span>, <span class="st">&quot;Other&quot;</span>),</span>
<span id="cb78-222"><a href="#cb78-222" aria-hidden="true" tabindex="-1"></a>    <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;F. EQ_&quot;</span>, <span class="st">&quot;Other&quot;</span>),</span>
<span id="cb78-223"><a href="#cb78-223" aria-hidden="true" tabindex="-1"></a>        <span class="at">indicator=</span><span class="fu">replace</span>(indicator, variable<span class="sc">==</span><span class="st">&quot;G. Honesty_&quot;</span>, <span class="st">&quot;Other&quot;</span>)</span>
<span id="cb78-224"><a href="#cb78-224" aria-hidden="true" tabindex="-1"></a>      )  <span class="sc">%&gt;%</span></span>
<span id="cb78-225"><a href="#cb78-225" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(    </span>
<span id="cb78-226"><a href="#cb78-226" aria-hidden="true" tabindex="-1"></a>  <span class="at">level=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(level, </span>
<span id="cb78-227"><a href="#cb78-227" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Prevention Effective    &quot;</span><span class="ot">=</span><span class="st">&quot;Yes    &quot;</span>, </span>
<span id="cb78-228"><a href="#cb78-228" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Prevention Not Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot;No    &quot;</span>,</span>
<span id="cb78-229"><a href="#cb78-229" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Relief Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot; Yes    &quot;</span>,</span>
<span id="cb78-230"><a href="#cb78-230" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;No Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot; No    &quot;</span>,</span>
<span id="cb78-231"><a href="#cb78-231" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Prevention Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot;High    &quot;</span>,</span>
<span id="cb78-232"><a href="#cb78-232" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;No Prevention Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot;Low    &quot;</span>,</span>
<span id="cb78-233"><a href="#cb78-233" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Relief Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot; High   &quot;</span>,</span>
<span id="cb78-234"><a href="#cb78-234" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;No Relief Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot; Low    &quot;</span>,</span>
<span id="cb78-235"><a href="#cb78-235" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Ask Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot;  Yes    &quot;</span>,</span>
<span id="cb78-236"><a href="#cb78-236" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Not Ask Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot;  No    &quot;</span>,</span>
<span id="cb78-237"><a href="#cb78-237" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;No Visits    &quot;</span> <span class="ot">=</span> <span class="st">&quot;   No    &quot;</span>,</span>
<span id="cb78-238"><a href="#cb78-238" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Visits    &quot;</span> <span class="ot">=</span> <span class="st">&quot;   Yes    &quot;</span></span>
<span id="cb78-239"><a href="#cb78-239" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-240"><a href="#cb78-240" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb78-241"><a href="#cb78-241" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-242"><a href="#cb78-242" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-243"><a href="#cb78-243" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-244"><a href="#cb78-244" aria-hidden="true" tabindex="-1"></a>res<span class="sc">$</span>level <span class="ot">&lt;-</span> <span class="fu">factor</span>(res<span class="sc">$</span>level, <span class="at">levels =</span> res<span class="sc">$</span>level)</span>
<span id="cb78-245"><a href="#cb78-245" aria-hidden="true" tabindex="-1"></a>res<span class="ot">&lt;-</span> res <span class="sc">%&gt;%</span>  <span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_rev</span>(<span class="fu">as_factor</span>(level))) </span>
<span id="cb78-246"><a href="#cb78-246" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-247"><a href="#cb78-247" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-248"><a href="#cb78-248" aria-hidden="true" tabindex="-1"></a>bold.ticks <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Ask Help&quot;</span>,<span class="st">&quot;Corruption&quot;</span>,<span class="st">&quot;Visits&quot;</span>,<span class="st">&quot;Effective Preparedness&quot;</span>,<span class="st">&quot;Effective Relief&quot;</span>,<span class="st">&quot;Effort Preparedness&quot;</span>,<span class="st">&quot;Effort Relief&quot;</span>)</span>
<span id="cb78-249"><a href="#cb78-249" aria-hidden="true" tabindex="-1"></a>bold.labels <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">levels</span>(res<span class="sc">$</span>level) <span class="sc">%in%</span> bold.ticks, <span class="at">yes =</span> <span class="st">&quot;bold&quot;</span>, <span class="at">no =</span> <span class="st">&quot;plain&quot;</span>)</span>
<span id="cb78-250"><a href="#cb78-250" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-251"><a href="#cb78-251" aria-hidden="true" tabindex="-1"></a>fig1<span class="ot">&lt;-</span>res <span class="sc">%&gt;%</span>  </span>
<span id="cb78-252"><a href="#cb78-252" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span>  level,<span class="at">color =</span> indicator)) <span class="sc">+</span></span>
<span id="cb78-253"><a href="#cb78-253" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-254"><a href="#cb78-254" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> level, </span>
<span id="cb78-255"><a href="#cb78-255" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> conf.low, </span>
<span id="cb78-256"><a href="#cb78-256" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> conf.high),</span>
<span id="cb78-257"><a href="#cb78-257" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="dv">1</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-258"><a href="#cb78-258" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_colour_colorblind</span>()<span class="sc">+</span></span>
<span id="cb78-259"><a href="#cb78-259" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_fill_viridis_b(name = &#39;indicator&#39;) + </span></span>
<span id="cb78-260"><a href="#cb78-260" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-261"><a href="#cb78-261" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;AMCEs&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-262"><a href="#cb78-262" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Effect on Probability of Support for Candidate&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-263"><a href="#cb78-263" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Disaster Policy Choice&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-264"><a href="#cb78-264" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-265"><a href="#cb78-265" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-266"><a href="#cb78-266" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">face=</span>bold.labels))<span class="sc">+</span></span>
<span id="cb78-267"><a href="#cb78-267" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>,<span class="at">vjust=</span><span class="sc">-</span><span class="fl">2.5</span>))<span class="sc">+</span></span>
<span id="cb78-268"><a href="#cb78-268" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-269"><a href="#cb78-269" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-270"><a href="#cb78-270" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;right&quot;</span>)</span>
<span id="cb78-271"><a href="#cb78-271" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-272"><a href="#cb78-272" aria-hidden="true" tabindex="-1"></a>fig1</span>
<span id="cb78-273"><a href="#cb78-273" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_1.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-274"><a href="#cb78-274" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-275"><a href="#cb78-275" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure 2: Marginal Effect of Prevention Effort Conditional on Effectiveness.</span></span>
<span id="cb78-276"><a href="#cb78-276" aria-hidden="true" tabindex="-1"></a><span class="in">```{r,warning=FALSE}</span></span>
<span id="cb78-277"><a href="#cb78-277" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-278"><a href="#cb78-278" aria-hidden="true" tabindex="-1"></a><span class="co"># Q: Does support for prevention efforts gets lower if voters see it as not being effective?</span></span>
<span id="cb78-279"><a href="#cb78-279" aria-hidden="true" tabindex="-1"></a><span class="co"># create indicator for occurrence of combination across rows</span></span>
<span id="cb78-280"><a href="#cb78-280" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-281"><a href="#cb78-281" aria-hidden="true" tabindex="-1"></a>df_long<span class="ot">&lt;-</span>  df_long <span class="sc">%&gt;%</span></span>
<span id="cb78-282"><a href="#cb78-282" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-283"><a href="#cb78-283" aria-hidden="true" tabindex="-1"></a>    <span class="at">Effort_prevention_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-284"><a href="#cb78-284" aria-hidden="true" tabindex="-1"></a>      Effort_prevention <span class="sc">==</span> <span class="st">&quot;No Prevention Effort&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-285"><a href="#cb78-285" aria-hidden="true" tabindex="-1"></a>      Effort_prevention <span class="sc">==</span> <span class="st">&quot;Prevention Effort&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-286"><a href="#cb78-286" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb78-287"><a href="#cb78-287" aria-hidden="true" tabindex="-1"></a>    <span class="at">Effort_relief_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-288"><a href="#cb78-288" aria-hidden="true" tabindex="-1"></a>      Effort_relief <span class="sc">==</span> <span class="st">&quot;No Relief Effort&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-289"><a href="#cb78-289" aria-hidden="true" tabindex="-1"></a>      Effort_relief <span class="sc">==</span> <span class="st">&quot;Relief Effort&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-290"><a href="#cb78-290" aria-hidden="true" tabindex="-1"></a>      ),</span>
<span id="cb78-291"><a href="#cb78-291" aria-hidden="true" tabindex="-1"></a>    <span class="at">Effective_prevention_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-292"><a href="#cb78-292" aria-hidden="true" tabindex="-1"></a>      Effective_prevention <span class="sc">==</span> <span class="st">&quot;Prevention Not Effective&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-293"><a href="#cb78-293" aria-hidden="true" tabindex="-1"></a>      Effective_prevention <span class="sc">==</span> <span class="st">&quot;Prevention Effective&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-294"><a href="#cb78-294" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb78-295"><a href="#cb78-295" aria-hidden="true" tabindex="-1"></a>    <span class="at">Effective_relief_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-296"><a href="#cb78-296" aria-hidden="true" tabindex="-1"></a>      Effective_relief <span class="sc">==</span> <span class="st">&quot;No Relief&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-297"><a href="#cb78-297" aria-hidden="true" tabindex="-1"></a>      Effective_relief <span class="sc">==</span> <span class="st">&quot;Relief Effective&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-298"><a href="#cb78-298" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb78-299"><a href="#cb78-299" aria-hidden="true" tabindex="-1"></a>    <span class="at">Ask_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-300"><a href="#cb78-300" aria-hidden="true" tabindex="-1"></a>      Ask <span class="sc">==</span> <span class="st">&quot;Not Ask Relief&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-301"><a href="#cb78-301" aria-hidden="true" tabindex="-1"></a>      Ask <span class="sc">==</span> <span class="st">&quot;Ask Relief&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-302"><a href="#cb78-302" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb78-303"><a href="#cb78-303" aria-hidden="true" tabindex="-1"></a>    <span class="at">EQ_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-304"><a href="#cb78-304" aria-hidden="true" tabindex="-1"></a>      EQ <span class="sc">==</span> <span class="st">&quot;No Visits&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-305"><a href="#cb78-305" aria-hidden="true" tabindex="-1"></a>      EQ <span class="sc">==</span> <span class="st">&quot;Visits&quot;</span>  <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-306"><a href="#cb78-306" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb78-307"><a href="#cb78-307" aria-hidden="true" tabindex="-1"></a>  <span class="at">Corruption_num =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-308"><a href="#cb78-308" aria-hidden="true" tabindex="-1"></a>    Honesty <span class="sc">==</span> <span class="st">&quot;No Corruption&quot;</span>  <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-309"><a href="#cb78-309" aria-hidden="true" tabindex="-1"></a>    Honesty <span class="sc">==</span> <span class="st">&quot;Corruption&quot;</span>  <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb78-310"><a href="#cb78-310" aria-hidden="true" tabindex="-1"></a>    Honesty <span class="sc">==</span> <span class="st">&quot;Vote Buying&quot;</span>  <span class="sc">~</span> <span class="dv">2</span></span>
<span id="cb78-311"><a href="#cb78-311" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-312"><a href="#cb78-312" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-313"><a href="#cb78-313" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-314"><a href="#cb78-314" aria-hidden="true" tabindex="-1"></a>  <span class="co"># count prevention effort + not effective by respondent</span></span>
<span id="cb78-315"><a href="#cb78-315" aria-hidden="true" tabindex="-1"></a>  df_long<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> </span>
<span id="cb78-316"><a href="#cb78-316" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">prevention_ineffective =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(Effort_prevention_num <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> Effective_prevention_num <span class="sc">==</span> <span class="dv">0</span> <span class="sc">~</span> <span class="dv">1</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-317"><a href="#cb78-317" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">prevention_ineffective =</span> <span class="fu">replace_na</span>(prevention_ineffective, <span class="dv">0</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-318"><a href="#cb78-318" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(CaseID) <span class="sc">%&gt;%</span> </span>
<span id="cb78-319"><a href="#cb78-319" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Frequency_prevention_ineffective =</span> <span class="fu">sum</span>(prevention_ineffective))</span>
<span id="cb78-320"><a href="#cb78-320" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-321"><a href="#cb78-321" aria-hidden="true" tabindex="-1"></a>  df_long<span class="sc">$</span>Frequency_prevention_ineffective <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long<span class="sc">$</span>Frequency_prevention_ineffective) </span>
<span id="cb78-322"><a href="#cb78-322" aria-hidden="true" tabindex="-1"></a>  df_long<span class="sc">$</span>Effort_prevention_num <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long<span class="sc">$</span>Effort_prevention_num) </span>
<span id="cb78-323"><a href="#cb78-323" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-324"><a href="#cb78-324" aria-hidden="true" tabindex="-1"></a>  prevention_effort_ineffective<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span></span>
<span id="cb78-325"><a href="#cb78-325" aria-hidden="true" tabindex="-1"></a>  <span class="fu">cj</span>(Choice <span class="sc">~</span> Effort_prevention_num ,<span class="at">id =</span> <span class="sc">~</span> CaseID, <span class="at">estimate =</span> <span class="st">&quot;amce&quot;</span>,<span class="at">by =</span> <span class="sc">~</span>Frequency_prevention_ineffective) <span class="sc">%&gt;%</span>  </span>
<span id="cb78-326"><a href="#cb78-326" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(level <span class="sc">==</span> <span class="dv">1</span>)</span>
<span id="cb78-327"><a href="#cb78-327" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-328"><a href="#cb78-328" aria-hidden="true" tabindex="-1"></a>  prev_ineff<span class="ot">&lt;-</span>prevention_effort_ineffective <span class="sc">%&gt;%</span></span>
<span id="cb78-329"><a href="#cb78-329" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> BY)) <span class="sc">+</span></span>
<span id="cb78-330"><a href="#cb78-330" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-331"><a href="#cb78-331" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> BY, </span>
<span id="cb78-332"><a href="#cb78-332" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> estimate <span class="sc">-</span> <span class="fl">1.96</span><span class="sc">*</span>std.error, </span>
<span id="cb78-333"><a href="#cb78-333" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> estimate <span class="sc">+</span> <span class="fl">1.96</span><span class="sc">*</span>std.error),</span>
<span id="cb78-334"><a href="#cb78-334" aria-hidden="true" tabindex="-1"></a>                 <span class="at">linewidth=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-335"><a href="#cb78-335" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_colour_viridis_d</span>(<span class="at">name =</span> <span class="st">&#39;Group&#39;</span>) <span class="sc">+</span> </span>
<span id="cb78-336"><a href="#cb78-336" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()<span class="sc">+</span></span>
<span id="cb78-337"><a href="#cb78-337" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-338"><a href="#cb78-338" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;AMCEs: Prevention Effort&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-339"><a href="#cb78-339" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.5</span>, <span class="dv">1</span>))<span class="sc">+</span></span>
<span id="cb78-340"><a href="#cb78-340" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Estimate&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;Number of Profiles: High Effort + No Outcome&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-341"><a href="#cb78-341" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-342"><a href="#cb78-342" aria-hidden="true" tabindex="-1"></a>  <span class="co">#theme(axis.text.x = element_text(size = 12)) +</span></span>
<span id="cb78-343"><a href="#cb78-343" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-344"><a href="#cb78-344" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)</span>
<span id="cb78-345"><a href="#cb78-345" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-346"><a href="#cb78-346" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-347"><a href="#cb78-347" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Prevention effort + effective</span></span>
<span id="cb78-348"><a href="#cb78-348" aria-hidden="true" tabindex="-1"></a>  df_long<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> </span>
<span id="cb78-349"><a href="#cb78-349" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">prevention_effective =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(Effort_prevention_num <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> Effective_prevention_num <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">1</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-350"><a href="#cb78-350" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">prevention_effective =</span> <span class="fu">replace_na</span>(prevention_effective, <span class="dv">0</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-351"><a href="#cb78-351" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(CaseID) <span class="sc">%&gt;%</span> </span>
<span id="cb78-352"><a href="#cb78-352" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Frequency_prevention_effective =</span> <span class="fu">sum</span>(prevention_effective))</span>
<span id="cb78-353"><a href="#cb78-353" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-354"><a href="#cb78-354" aria-hidden="true" tabindex="-1"></a>  df_long<span class="sc">$</span>Frequency_prevention_effective <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long<span class="sc">$</span>Frequency_prevention_effective) </span>
<span id="cb78-355"><a href="#cb78-355" aria-hidden="true" tabindex="-1"></a>  df_long<span class="sc">$</span>Effort_prevention_num <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long<span class="sc">$</span>Effort_prevention_num) </span>
<span id="cb78-356"><a href="#cb78-356" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-357"><a href="#cb78-357" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-358"><a href="#cb78-358" aria-hidden="true" tabindex="-1"></a>  prevention_eff<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> </span>
<span id="cb78-359"><a href="#cb78-359" aria-hidden="true" tabindex="-1"></a>  <span class="co"># could exclude contest 8 (too few cases) and cluster by CaseID, results do not change</span></span>
<span id="cb78-360"><a href="#cb78-360" aria-hidden="true" tabindex="-1"></a>    <span class="fu">cj</span>( Choice <span class="sc">~</span> Effort_prevention_num ,<span class="at">estimate =</span> <span class="st">&quot;amce&quot;</span>,<span class="at">by =</span> <span class="sc">~</span>Frequency_prevention_effective) <span class="sc">%&gt;%</span> </span>
<span id="cb78-361"><a href="#cb78-361" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">filter</span>(level <span class="sc">==</span> <span class="dv">1</span>)</span>
<span id="cb78-362"><a href="#cb78-362" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-363"><a href="#cb78-363" aria-hidden="true" tabindex="-1"></a>  prev_eff<span class="ot">&lt;-</span>prevention_eff <span class="sc">%&gt;%</span></span>
<span id="cb78-364"><a href="#cb78-364" aria-hidden="true" tabindex="-1"></a>  <span class="co">#mutate(level = fct_reorder(estimate, desc(BY))) %&gt;%</span></span>
<span id="cb78-365"><a href="#cb78-365" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> BY)) <span class="sc">+</span></span>
<span id="cb78-366"><a href="#cb78-366" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-367"><a href="#cb78-367" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> BY, </span>
<span id="cb78-368"><a href="#cb78-368" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> estimate <span class="sc">-</span> <span class="fl">1.96</span><span class="sc">*</span>std.error, </span>
<span id="cb78-369"><a href="#cb78-369" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> estimate <span class="sc">+</span> <span class="fl">1.96</span><span class="sc">*</span>std.error),</span>
<span id="cb78-370"><a href="#cb78-370" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-371"><a href="#cb78-371" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_colour_viridis_d</span>(<span class="at">name =</span> <span class="st">&#39;Group&#39;</span>) <span class="sc">+</span> </span>
<span id="cb78-372"><a href="#cb78-372" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()<span class="sc">+</span></span>
<span id="cb78-373"><a href="#cb78-373" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-374"><a href="#cb78-374" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;AMCEs: Prevention Effort&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-375"><a href="#cb78-375" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.5</span>, <span class="dv">1</span>))<span class="sc">+</span></span>
<span id="cb78-376"><a href="#cb78-376" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-377"><a href="#cb78-377" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Estimate&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;Number of Profiles: High Effort + Effective Outcome&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-378"><a href="#cb78-378" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-379"><a href="#cb78-379" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)</span>
<span id="cb78-380"><a href="#cb78-380" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-381"><a href="#cb78-381" aria-hidden="true" tabindex="-1"></a>fig_updating  <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(prev_ineff,prev_eff,</span>
<span id="cb78-382"><a href="#cb78-382" aria-hidden="true" tabindex="-1"></a>          <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>, <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-383"><a href="#cb78-383" aria-hidden="true" tabindex="-1"></a>fig_updating</span>
<span id="cb78-384"><a href="#cb78-384" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-385"><a href="#cb78-385" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_2_updating.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">9</span>, <span class="at">height =</span> <span class="dv">3</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-386"><a href="#cb78-386" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-387"><a href="#cb78-387" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-388"><a href="#cb78-388" aria-hidden="true" tabindex="-1"></a><span class="fu"># Online Appendix</span></span>
<span id="cb78-389"><a href="#cb78-389" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-390"><a href="#cb78-390" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A1: Natural Disasters across Africa 1970-2020</span></span>
<span id="cb78-391"><a href="#cb78-391" aria-hidden="true" tabindex="-1"></a><span class="in">```{r,warning=FALSE}</span></span>
<span id="cb78-392"><a href="#cb78-392" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-393"><a href="#cb78-393" aria-hidden="true" tabindex="-1"></a><span class="co"># Total Number of Disasters by country</span></span>
<span id="cb78-394"><a href="#cb78-394" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-395"><a href="#cb78-395" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-396"><a href="#cb78-396" aria-hidden="true" tabindex="-1"></a>emdat <span class="ot">&lt;-</span> <span class="fu">read_excel</span>(<span class="st">&quot;1_input/emdat_public_2020_12_11_query_uid-GZ254X.xlsx&quot;</span>)</span>
<span id="cb78-397"><a href="#cb78-397" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-398"><a href="#cb78-398" aria-hidden="true" tabindex="-1"></a><span class="co"># change name </span></span>
<span id="cb78-399"><a href="#cb78-399" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(emdat)[<span class="fu">names</span>(emdat) <span class="sc">==</span> <span class="st">&#39;Disaster Type&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;Disaster&#39;</span></span>
<span id="cb78-400"><a href="#cb78-400" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-401"><a href="#cb78-401" aria-hidden="true" tabindex="-1"></a><span class="co"># keep most common disaster (scale gets to messy otherwise)</span></span>
<span id="cb78-402"><a href="#cb78-402" aria-hidden="true" tabindex="-1"></a>emdat <span class="ot">&lt;-</span> emdat[ <span class="fu">which</span>(emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Flood&#39;</span>  <span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Drought&#39;</span> <span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Storm&#39;</span> <span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Wildfire&#39;</span> <span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Insect infestation&#39;</span><span class="sc">|</span> emdat<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Extreme temperature&#39;</span>), ]</span>
<span id="cb78-403"><a href="#cb78-403" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-404"><a href="#cb78-404" aria-hidden="true" tabindex="-1"></a><span class="co"># subset, 1970 +</span></span>
<span id="cb78-405"><a href="#cb78-405" aria-hidden="true" tabindex="-1"></a>emdat<span class="ot">&lt;-</span>emdat <span class="sc">%&gt;%</span> dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="fu">as.numeric</span>(Year) <span class="sc">&gt;</span> <span class="dv">1969</span>)</span>
<span id="cb78-406"><a href="#cb78-406" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-407"><a href="#cb78-407" aria-hidden="true" tabindex="-1"></a><span class="co"># create event variable for disaster</span></span>
<span id="cb78-408"><a href="#cb78-408" aria-hidden="true" tabindex="-1"></a>emdat<span class="sc">$</span>total <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="dv">1</span>,<span class="fu">nrow</span>(emdat)) </span>
<span id="cb78-409"><a href="#cb78-409" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-410"><a href="#cb78-410" aria-hidden="true" tabindex="-1"></a><span class="co"># aggregate by group year </span></span>
<span id="cb78-411"><a href="#cb78-411" aria-hidden="true" tabindex="-1"></a>DisasterAggTypeYear<span class="ot">&lt;-</span>emdat <span class="sc">%&gt;%</span> </span>
<span id="cb78-412"><a href="#cb78-412" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Year, Disaster) <span class="sc">%&gt;%</span> </span>
<span id="cb78-413"><a href="#cb78-413" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(total), <span class="fu">list</span>(<span class="sc">~</span><span class="fu">sum</span>(.)))</span>
<span id="cb78-414"><a href="#cb78-414" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-415"><a href="#cb78-415" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb78-416"><a href="#cb78-416" aria-hidden="true" tabindex="-1"></a>fig_disaster_time<span class="ot">&lt;-</span><span class="fu">ggplot</span>() <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">y =</span> total, <span class="at">x =</span><span class="fu">as.numeric</span>(Year), <span class="at">fill =</span> Disaster),</span>
<span id="cb78-417"><a href="#cb78-417" aria-hidden="true" tabindex="-1"></a>                      <span class="at">data =</span> DisasterAggTypeYear,</span>
<span id="cb78-418"><a href="#cb78-418" aria-hidden="true" tabindex="-1"></a>                      <span class="at">stat=</span><span class="st">&quot;identity&quot;</span>) <span class="sc">+</span> </span>
<span id="cb78-419"><a href="#cb78-419" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">1970</span>, <span class="dv">2020</span>, <span class="at">by =</span> <span class="dv">10</span>)) <span class="sc">+</span></span>
<span id="cb78-420"><a href="#cb78-420" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb78-421"><a href="#cb78-421" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis_d</span>() <span class="sc">+</span></span>
<span id="cb78-422"><a href="#cb78-422" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_fill_grey()+</span></span>
<span id="cb78-423"><a href="#cb78-423" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb78-424"><a href="#cb78-424" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.justification=</span><span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb78-425"><a href="#cb78-425" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.margin=</span><span class="fu">margin</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>),</span>
<span id="cb78-426"><a href="#cb78-426" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.box.margin=</span><span class="fu">margin</span>(<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>))<span class="sc">+</span></span>
<span id="cb78-427"><a href="#cb78-427" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">y =</span><span class="st">&quot;Number of Disasters&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-428"><a href="#cb78-428" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Africa: Disasters&quot;</span>)</span>
<span id="cb78-429"><a href="#cb78-429" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-430"><a href="#cb78-430" aria-hidden="true" tabindex="-1"></a><span class="do">##################################</span></span>
<span id="cb78-431"><a href="#cb78-431" aria-hidden="true" tabindex="-1"></a><span class="co"># Malawi data</span></span>
<span id="cb78-432"><a href="#cb78-432" aria-hidden="true" tabindex="-1"></a><span class="do">##################################</span></span>
<span id="cb78-433"><a href="#cb78-433" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-434"><a href="#cb78-434" aria-hidden="true" tabindex="-1"></a>Malawi <span class="ot">&lt;-</span> emdat[ <span class="fu">which</span>(emdat<span class="sc">$</span>Country<span class="sc">==</span><span class="st">&#39;Malawi&#39;</span>), ]</span>
<span id="cb78-435"><a href="#cb78-435" aria-hidden="true" tabindex="-1"></a>Malawi_flood <span class="ot">&lt;-</span> Malawi[ <span class="fu">which</span>(Malawi<span class="sc">$</span>Disaster<span class="sc">==</span><span class="st">&#39;Flood&#39;</span>), ]</span>
<span id="cb78-436"><a href="#cb78-436" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-437"><a href="#cb78-437" aria-hidden="true" tabindex="-1"></a><span class="co"># create event variable for disaster</span></span>
<span id="cb78-438"><a href="#cb78-438" aria-hidden="true" tabindex="-1"></a>emdat<span class="sc">$</span>total <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="dv">1</span>,<span class="fu">nrow</span>(emdat)) </span>
<span id="cb78-439"><a href="#cb78-439" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-440"><a href="#cb78-440" aria-hidden="true" tabindex="-1"></a>Malawi_agg_flood<span class="ot">&lt;-</span>Malawi_flood <span class="sc">%&gt;%</span> </span>
<span id="cb78-441"><a href="#cb78-441" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Year) <span class="sc">%&gt;%</span> </span>
<span id="cb78-442"><a href="#cb78-442" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(total), <span class="fu">list</span>(<span class="sc">~</span><span class="fu">sum</span>(.)))</span>
<span id="cb78-443"><a href="#cb78-443" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-444"><a href="#cb78-444" aria-hidden="true" tabindex="-1"></a>Malawi_agg_year<span class="ot">&lt;-</span>Malawi <span class="sc">%&gt;%</span> </span>
<span id="cb78-445"><a href="#cb78-445" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Year) <span class="sc">%&gt;%</span> </span>
<span id="cb78-446"><a href="#cb78-446" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(total), <span class="fu">list</span>(<span class="sc">~</span><span class="fu">sum</span>(.)))</span>
<span id="cb78-447"><a href="#cb78-447" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-448"><a href="#cb78-448" aria-hidden="true" tabindex="-1"></a><span class="co"># fill time series gaps</span></span>
<span id="cb78-449"><a href="#cb78-449" aria-hidden="true" tabindex="-1"></a>Year<span class="ot">&lt;-</span><span class="fu">seq</span>(<span class="dv">1960</span>, <span class="dv">2020</span>, <span class="dv">1</span>,)</span>
<span id="cb78-450"><a href="#cb78-450" aria-hidden="true" tabindex="-1"></a>Full_years<span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Year)</span>
<span id="cb78-451"><a href="#cb78-451" aria-hidden="true" tabindex="-1"></a>Malawi_agg_year<span class="sc">$</span>Year <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(Malawi_agg_year<span class="sc">$</span>Year)</span>
<span id="cb78-452"><a href="#cb78-452" aria-hidden="true" tabindex="-1"></a>Malawi_agg_flood<span class="sc">$</span>Year <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(Malawi_agg_flood<span class="sc">$</span>Year)</span>
<span id="cb78-453"><a href="#cb78-453" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-454"><a href="#cb78-454" aria-hidden="true" tabindex="-1"></a><span class="co"># merge</span></span>
<span id="cb78-455"><a href="#cb78-455" aria-hidden="true" tabindex="-1"></a>Malawi_agg_year_full<span class="ot">&lt;-</span> Full_years <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(Malawi_agg_year,<span class="at">by=</span><span class="fu">c</span>(<span class="st">&quot;Year&quot;</span>))</span>
<span id="cb78-456"><a href="#cb78-456" aria-hidden="true" tabindex="-1"></a>Malawi_agg_flood_full<span class="ot">&lt;-</span> Full_years <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(Malawi_agg_flood,<span class="at">by=</span><span class="fu">c</span>(<span class="st">&quot;Year&quot;</span>))</span>
<span id="cb78-457"><a href="#cb78-457" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-458"><a href="#cb78-458" aria-hidden="true" tabindex="-1"></a><span class="co"># fill in NA as 0</span></span>
<span id="cb78-459"><a href="#cb78-459" aria-hidden="true" tabindex="-1"></a>Malawi_agg_year_full <span class="ot">&lt;-</span> Malawi_agg_year_full <span class="sc">%&gt;%</span></span>
<span id="cb78-460"><a href="#cb78-460" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">total_full =</span> <span class="fu">if_else</span>(<span class="fu">is.na</span>(total), <span class="dv">0</span>, total))</span>
<span id="cb78-461"><a href="#cb78-461" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-462"><a href="#cb78-462" aria-hidden="true" tabindex="-1"></a>Malawi_agg_flood_full <span class="ot">&lt;-</span> Malawi_agg_flood_full <span class="sc">%&gt;%</span></span>
<span id="cb78-463"><a href="#cb78-463" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">flood =</span> <span class="fu">if_else</span>(<span class="fu">is.na</span>(total), <span class="dv">0</span>, total))</span>
<span id="cb78-464"><a href="#cb78-464" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-465"><a href="#cb78-465" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-466"><a href="#cb78-466" aria-hidden="true" tabindex="-1"></a>Malawi_final<span class="ot">&lt;-</span> Malawi_agg_year_full <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(Malawi_agg_flood_full,<span class="at">by=</span><span class="fu">c</span>(<span class="st">&quot;Year&quot;</span>))</span>
<span id="cb78-467"><a href="#cb78-467" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-468"><a href="#cb78-468" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-469"><a href="#cb78-469" aria-hidden="true" tabindex="-1"></a><span class="co"># create three year moving averages, otherwise not so informative </span></span>
<span id="cb78-470"><a href="#cb78-470" aria-hidden="true" tabindex="-1"></a>Malawi_final <span class="ot">&lt;-</span> Malawi_final <span class="sc">%&gt;%</span></span>
<span id="cb78-471"><a href="#cb78-471" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Year, <span class="at">srate =</span> total_full, <span class="at">srate2 =</span> flood) <span class="sc">%&gt;%</span></span>
<span id="cb78-472"><a href="#cb78-472" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-473"><a href="#cb78-473" aria-hidden="true" tabindex="-1"></a>    <span class="at">All =</span> zoo<span class="sc">::</span><span class="fu">rollmean</span>(srate, <span class="at">k =</span> <span class="dv">5</span>, <span class="at">fill =</span> <span class="cn">NA</span>, <span class="at">align =</span> <span class="st">&quot;center&quot;</span>),</span>
<span id="cb78-474"><a href="#cb78-474" aria-hidden="true" tabindex="-1"></a>    <span class="at">Flood =</span> zoo<span class="sc">::</span><span class="fu">rollmean</span>(srate2, <span class="at">k =</span> <span class="dv">5</span>, <span class="at">fill =</span> <span class="cn">NA</span>, <span class="at">align =</span> <span class="st">&quot;center&quot;</span>)</span>
<span id="cb78-475"><a href="#cb78-475" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-476"><a href="#cb78-476" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-477"><a href="#cb78-477" aria-hidden="true" tabindex="-1"></a><span class="co"># melt </span></span>
<span id="cb78-478"><a href="#cb78-478" aria-hidden="true" tabindex="-1"></a>Malawi_final<span class="ot">&lt;-</span><span class="fu">melt</span>(<span class="at">data =</span> Malawi_final, <span class="at">id.vars =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">measure.vars =</span> <span class="fu">c</span>(<span class="st">&quot;All&quot;</span>, <span class="st">&quot;Flood&quot;</span>))</span>
<span id="cb78-479"><a href="#cb78-479" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-480"><a href="#cb78-480" aria-hidden="true" tabindex="-1"></a><span class="co"># change names</span></span>
<span id="cb78-481"><a href="#cb78-481" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(Malawi_final)[<span class="fu">names</span>(Malawi_final) <span class="sc">==</span> <span class="st">&quot;variable&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Disaster&quot;</span></span>
<span id="cb78-482"><a href="#cb78-482" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-483"><a href="#cb78-483" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-484"><a href="#cb78-484" aria-hidden="true" tabindex="-1"></a><span class="co">#Plot by year</span></span>
<span id="cb78-485"><a href="#cb78-485" aria-hidden="true" tabindex="-1"></a>fig_disaster_malawi_time<span class="ot">&lt;-</span><span class="fu">ggplot</span>(<span class="at">data =</span> Malawi_final) <span class="sc">+</span></span>
<span id="cb78-486"><a href="#cb78-486" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> value, <span class="at">x =</span> Year , <span class="at">colour=</span>Disaster), </span>
<span id="cb78-487"><a href="#cb78-487" aria-hidden="true" tabindex="-1"></a>            <span class="at">alpha =</span> <span class="fl">0.6</span>,</span>
<span id="cb78-488"><a href="#cb78-488" aria-hidden="true" tabindex="-1"></a>            <span class="at">size =</span> <span class="fl">0.6</span>) <span class="sc">+</span></span>
<span id="cb78-489"><a href="#cb78-489" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis</span>() <span class="sc">+</span></span>
<span id="cb78-490"><a href="#cb78-490" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_color_manual(values = c(&quot;gray40&quot;,&quot;#09557f&quot;)) +</span></span>
<span id="cb78-491"><a href="#cb78-491" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb78-492"><a href="#cb78-492" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb78-493"><a href="#cb78-493" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.justification=</span><span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb78-494"><a href="#cb78-494" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.margin=</span><span class="fu">margin</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>),</span>
<span id="cb78-495"><a href="#cb78-495" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.box.margin=</span><span class="fu">margin</span>(<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>,<span class="sc">-</span><span class="dv">10</span>))<span class="sc">+</span></span>
<span id="cb78-496"><a href="#cb78-496" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">1970</span>, <span class="dv">2020</span>, <span class="at">by =</span> <span class="dv">10</span>)) <span class="sc">+</span></span>
<span id="cb78-497"><a href="#cb78-497" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>, <span class="dv">5</span>)) <span class="sc">+</span></span>
<span id="cb78-498"><a href="#cb78-498" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">y =</span><span class="st">&quot;Number of Disasters&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-499"><a href="#cb78-499" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Malawi: Disasters&quot;</span>)</span>
<span id="cb78-500"><a href="#cb78-500" aria-hidden="true" tabindex="-1"></a>  <span class="co">#xlim(c(1965,2020))+ </span></span>
<span id="cb78-501"><a href="#cb78-501" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_fill_discrete(name = &quot;New Legend Title&quot;)</span></span>
<span id="cb78-502"><a href="#cb78-502" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-503"><a href="#cb78-503" aria-hidden="true" tabindex="-1"></a>fig_disaster_timeseries<span class="ot">&lt;-</span><span class="fu">grid.arrange</span>(fig_disaster_time,fig_disaster_malawi_time , <span class="at">ncol =</span> <span class="dv">2</span>)</span>
<span id="cb78-504"><a href="#cb78-504" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-505"><a href="#cb78-505" aria-hidden="true" tabindex="-1"></a>fig_disaster_timeseries</span>
<span id="cb78-506"><a href="#cb78-506" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A1_disaster_timeseries.pdf&quot;</span>,<span class="at">width =</span> <span class="dv">10</span>, <span class="at">height =</span> <span class="dv">3</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>,fig_disaster_timeseries )</span>
<span id="cb78-507"><a href="#cb78-507" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-508"><a href="#cb78-508" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-509"><a href="#cb78-509" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-510"><a href="#cb78-510" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-511"><a href="#cb78-511" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A2: Number of Natural Disasters by Country (1970 and 2020) and Preparedness by Country (2007-2011).</span></span>
<span id="cb78-512"><a href="#cb78-512" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-515"><a href="#cb78-515" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb78-516"><a href="#cb78-516" aria-hidden="true" tabindex="-1"></a><span class="co"># Read GeoJSON file</span></span>
<span id="cb78-517"><a href="#cb78-517" aria-hidden="true" tabindex="-1"></a>africa_shp <span class="ot">&lt;-</span> <span class="fu">st_read</span>(<span class="st">&quot;1_input/custom.geo.json&quot;</span>)</span>
<span id="cb78-518"><a href="#cb78-518" aria-hidden="true" tabindex="-1"></a><span class="co"># Extract iso_a3 values from africa_shp</span></span>
<span id="cb78-519"><a href="#cb78-519" aria-hidden="true" tabindex="-1"></a>iso_a3_values <span class="ot">&lt;-</span> africa_shp <span class="sc">%&gt;%</span> </span>
<span id="cb78-520"><a href="#cb78-520" aria-hidden="true" tabindex="-1"></a>  <span class="fu">st_drop_geometry</span>() <span class="sc">%&gt;%</span>  <span class="co"># Remove geometry to just keep the data</span></span>
<span id="cb78-521"><a href="#cb78-521" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(iso_a3)  <span class="co"># Extract iso_a3 column</span></span>
<span id="cb78-522"><a href="#cb78-522" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-523"><a href="#cb78-523" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-524"><a href="#cb78-524" aria-hidden="true" tabindex="-1"></a><span class="co"># path in downloaded data: ND_Gain/resources/indicators/id_infr_06/raw0.csv</span></span>
<span id="cb78-525"><a href="#cb78-525" aria-hidden="true" tabindex="-1"></a>africa_data <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="at">file =</span> <span class="st">&#39;1_input/ND_Gain_id_infr_06_raw0.csv&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-526"><a href="#cb78-526" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">mean_col =</span> <span class="fu">rowMeans</span>(<span class="fu">select</span>(., X2007, X2009, X2011), <span class="at">na.rm =</span> <span class="cn">TRUE</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-527"><a href="#cb78-527" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(ISO3, Name, X2007, X2009, X2011, mean_col)<span class="sc">%&gt;%</span> </span>
<span id="cb78-528"><a href="#cb78-528" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(ISO3 <span class="sc">%in%</span> iso_a3_values)</span>
<span id="cb78-529"><a href="#cb78-529" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-530"><a href="#cb78-530" aria-hidden="true" tabindex="-1"></a><span class="co"># calculate points at which to plot labels</span></span>
<span id="cb78-531"><a href="#cb78-531" aria-hidden="true" tabindex="-1"></a>centroids <span class="ot">&lt;-</span> africa_shp <span class="sc">%&gt;%</span> </span>
<span id="cb78-532"><a href="#cb78-532" aria-hidden="true" tabindex="-1"></a>  <span class="fu">st_centroid</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb78-533"><a href="#cb78-533" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bind_cols</span>(<span class="fu">as_tibble</span>(<span class="fu">st_coordinates</span>(.)))    <span class="co"># unpack points to lat/lon columns</span></span>
<span id="cb78-534"><a href="#cb78-534" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-535"><a href="#cb78-535" aria-hidden="true" tabindex="-1"></a>centroids <span class="ot">&lt;-</span> centroids <span class="sc">%&gt;%</span></span>
<span id="cb78-536"><a href="#cb78-536" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">country.flag =</span> <span class="fu">ifelse</span>(iso_a3 <span class="sc">==</span> <span class="st">&quot;MWI&quot;</span>, <span class="st">&quot;MW&quot;</span>, <span class="cn">NA</span>)) <span class="co"># &quot;MW&quot; for Malawi</span></span>
<span id="cb78-537"><a href="#cb78-537" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-538"><a href="#cb78-538" aria-hidden="true" tabindex="-1"></a>spatial_preparedness_africa<span class="ot">&lt;-</span>africa_data <span class="sc">%&gt;%</span> </span>
<span id="cb78-539"><a href="#cb78-539" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(africa_shp, ., <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&#39;iso_a3&#39;</span> <span class="ot">=</span> <span class="st">&#39;ISO3&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-540"><a href="#cb78-540" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-541"><a href="#cb78-541" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_sf</span>(<span class="fu">aes</span>(<span class="at">fill =</span> mean_col)) <span class="sc">+</span> </span>
<span id="cb78-542"><a href="#cb78-542" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis</span>( <span class="at">name=</span><span class="st">&quot;Disaster Preparedness&quot;</span> ) <span class="sc">+</span></span>
<span id="cb78-543"><a href="#cb78-543" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="fu">aes</span>(X, Y, <span class="at">label =</span> country.flag), <span class="at">data =</span> centroids, <span class="at">size =</span> <span class="dv">4</span>, <span class="at">color =</span> <span class="st">&quot;red&quot;</span>, <span class="at">na.rm =</span> <span class="cn">TRUE</span>) <span class="sc">+</span></span>
<span id="cb78-544"><a href="#cb78-544" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb78-545"><a href="#cb78-545" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Latitude&quot;</span>) <span class="sc">+</span> </span>
<span id="cb78-546"><a href="#cb78-546" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Longitude&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-547"><a href="#cb78-547" aria-hidden="true" tabindex="-1"></a>  <span class="co">#ggtitle(&quot;Africa&quot;)+</span></span>
<span id="cb78-548"><a href="#cb78-548" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-549"><a href="#cb78-549" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-550"><a href="#cb78-550" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-551"><a href="#cb78-551" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-552"><a href="#cb78-552" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-553"><a href="#cb78-553" aria-hidden="true" tabindex="-1"></a><span class="co"># Total Number of Disasters by country</span></span>
<span id="cb78-554"><a href="#cb78-554" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-555"><a href="#cb78-555" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-556"><a href="#cb78-556" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-557"><a href="#cb78-557" aria-hidden="true" tabindex="-1"></a>agg_country<span class="ot">&lt;-</span>emdat <span class="sc">%&gt;%</span> </span>
<span id="cb78-558"><a href="#cb78-558" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(ISO) <span class="sc">%&gt;%</span> </span>
<span id="cb78-559"><a href="#cb78-559" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(total), <span class="fu">list</span>(<span class="sc">~</span><span class="fu">sum</span>(.)))</span>
<span id="cb78-560"><a href="#cb78-560" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-561"><a href="#cb78-561" aria-hidden="true" tabindex="-1"></a>spatial_conflict_africa<span class="ot">&lt;-</span>agg_country <span class="sc">%&gt;%</span> </span>
<span id="cb78-562"><a href="#cb78-562" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(africa_shp, ., <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&#39;iso_a3&#39;</span> <span class="ot">=</span> <span class="st">&#39;ISO&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-563"><a href="#cb78-563" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-564"><a href="#cb78-564" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_sf</span>(<span class="fu">aes</span>(<span class="at">fill =</span> total)) <span class="sc">+</span> </span>
<span id="cb78-565"><a href="#cb78-565" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis</span>(<span class="at">name=</span><span class="st">&quot;Total Disasters&quot;</span> ) <span class="sc">+</span></span>
<span id="cb78-566"><a href="#cb78-566" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="fu">aes</span>(X, Y, <span class="at">label =</span> country.flag), <span class="at">data =</span> centroids, <span class="at">size =</span> <span class="dv">4</span>, <span class="at">color =</span> <span class="st">&#39;red&#39;</span>)<span class="sc">+</span></span>
<span id="cb78-567"><a href="#cb78-567" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb78-568"><a href="#cb78-568" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Latitude&quot;</span>) <span class="sc">+</span> </span>
<span id="cb78-569"><a href="#cb78-569" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Longitude&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-570"><a href="#cb78-570" aria-hidden="true" tabindex="-1"></a>  <span class="co">#ggtitle(&quot;Africa: Total Number of Natural Disasters&quot;)+</span></span>
<span id="cb78-571"><a href="#cb78-571" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-572"><a href="#cb78-572" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-573"><a href="#cb78-573" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-574"><a href="#cb78-574" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-575"><a href="#cb78-575" aria-hidden="true" tabindex="-1"></a>fig_A2_spatial<span class="ot">&lt;-</span><span class="fu">grid.arrange</span>(spatial_conflict_africa,                             </span>
<span id="cb78-576"><a href="#cb78-576" aria-hidden="true" tabindex="-1"></a>                    spatial_preparedness_africa,</span>
<span id="cb78-577"><a href="#cb78-577" aria-hidden="true" tabindex="-1"></a>                    <span class="at">nrow =</span> <span class="dv">1</span>,<span class="at">ncol=</span><span class="dv">2</span>)        </span>
<span id="cb78-578"><a href="#cb78-578" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A2_spatial.pdf&quot;</span>,<span class="at">width =</span> <span class="dv">10</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>,fig_A2_spatial )</span>
<span id="cb78-579"><a href="#cb78-579" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-580"><a href="#cb78-580" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-581"><a href="#cb78-581" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-582"><a href="#cb78-582" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-583"><a href="#cb78-583" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-584"><a href="#cb78-584" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-585"><a href="#cb78-585" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A3: Malawi Disaster Spending and Perceived Responsibilities</span></span>
<span id="cb78-586"><a href="#cb78-586" aria-hidden="true" tabindex="-1"></a><span class="in">```{r,warning=FALSE}</span></span>
<span id="cb78-587"><a href="#cb78-587" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-588"><a href="#cb78-588" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-589"><a href="#cb78-589" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-590"><a href="#cb78-590" aria-hidden="true" tabindex="-1"></a><span class="co"># Relief Aid </span></span>
<span id="cb78-591"><a href="#cb78-591" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-592"><a href="#cb78-592" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-593"><a href="#cb78-593" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-594"><a href="#cb78-594" aria-hidden="true" tabindex="-1"></a><span class="co"># Humanitarian Responses: https://www.usaid.gov/sites/default/files/documents/1860/Malawi_National_Resilience_Strategy.pdf</span></span>
<span id="cb78-595"><a href="#cb78-595" aria-hidden="true" tabindex="-1"></a><span class="co"># Page: 10</span></span>
<span id="cb78-596"><a href="#cb78-596" aria-hidden="true" tabindex="-1"></a>Relief_million <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">2.5</span>, </span>
<span id="cb78-597"><a href="#cb78-597" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">5</span>, </span>
<span id="cb78-598"><a href="#cb78-598" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">7</span>,</span>
<span id="cb78-599"><a href="#cb78-599" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">25</span>,</span>
<span id="cb78-600"><a href="#cb78-600" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">20</span>,</span>
<span id="cb78-601"><a href="#cb78-601" aria-hidden="true" tabindex="-1"></a>                    <span class="fl">27.5</span>,</span>
<span id="cb78-602"><a href="#cb78-602" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">55</span>,</span>
<span id="cb78-603"><a href="#cb78-603" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">40</span>,</span>
<span id="cb78-604"><a href="#cb78-604" aria-hidden="true" tabindex="-1"></a>                    <span class="fl">57.5</span>,</span>
<span id="cb78-605"><a href="#cb78-605" aria-hidden="true" tabindex="-1"></a>                    <span class="fl">57.5</span>,</span>
<span id="cb78-606"><a href="#cb78-606" aria-hidden="true" tabindex="-1"></a>                    <span class="fl">57.5</span>,</span>
<span id="cb78-607"><a href="#cb78-607" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">62</span>,</span>
<span id="cb78-608"><a href="#cb78-608" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">45</span>,</span>
<span id="cb78-609"><a href="#cb78-609" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">30</span>,</span>
<span id="cb78-610"><a href="#cb78-610" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">80</span>,</span>
<span id="cb78-611"><a href="#cb78-611" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">100</span>,</span>
<span id="cb78-612"><a href="#cb78-612" aria-hidden="true" tabindex="-1"></a>                    <span class="dv">350</span>)</span>
<span id="cb78-613"><a href="#cb78-613" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-614"><a href="#cb78-614" aria-hidden="true" tabindex="-1"></a>Year<span class="ot">&lt;-</span><span class="fu">seq</span>(<span class="at">from =</span> <span class="dv">2000</span>, <span class="at">to =</span> <span class="dv">2016</span>, <span class="at">by=</span><span class="dv">1</span>)</span>
<span id="cb78-615"><a href="#cb78-615" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-616"><a href="#cb78-616" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-617"><a href="#cb78-617" aria-hidden="true" tabindex="-1"></a>response <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(<span class="fu">cbind</span>(Year,Relief_million))</span>
<span id="cb78-618"><a href="#cb78-618" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-619"><a href="#cb78-619" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-620"><a href="#cb78-620" aria-hidden="true" tabindex="-1"></a><span class="do">##########################################</span></span>
<span id="cb78-621"><a href="#cb78-621" aria-hidden="true" tabindex="-1"></a><span class="co"># Adaption (Prevention id)</span></span>
<span id="cb78-622"><a href="#cb78-622" aria-hidden="true" tabindex="-1"></a><span class="do">##########################################</span></span>
<span id="cb78-623"><a href="#cb78-623" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-624"><a href="#cb78-624" aria-hidden="true" tabindex="-1"></a>Malawi_Geocoded_Climate_Dataset <span class="ot">&lt;-</span> <span class="fu">read_excel</span>(<span class="st">&quot;1_input/Malawi Geocoded Climate Dataset.xlsx&quot;</span>, </span>
<span id="cb78-625"><a href="#cb78-625" aria-hidden="true" tabindex="-1"></a>                                              <span class="at">sheet =</span> <span class="st">&quot;All Projects&quot;</span>)</span>
<span id="cb78-626"><a href="#cb78-626" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-627"><a href="#cb78-627" aria-hidden="true" tabindex="-1"></a>climate_projects <span class="ot">&lt;-</span> <span class="fu">subset</span>(Malawi_Geocoded_Climate_Dataset, <span class="st">`</span><span class="at">Climate Spectrum Assignment</span><span class="st">`</span> <span class="sc">==</span><span class="st">&quot;Climate Oriented&quot;</span>)</span>
<span id="cb78-628"><a href="#cb78-628" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-629"><a href="#cb78-629" aria-hidden="true" tabindex="-1"></a><span class="co"># keep if amount specified</span></span>
<span id="cb78-630"><a href="#cb78-630" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects[<span class="sc">!</span><span class="fu">is.na</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Cumulative Disbursement</span><span class="st">`</span>), ]</span>
<span id="cb78-631"><a href="#cb78-631" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-632"><a href="#cb78-632" aria-hidden="true" tabindex="-1"></a><span class="co"># if date of signment is &quot;NA&quot; or &quot;0&quot;, use the completion date if possible</span></span>
<span id="cb78-633"><a href="#cb78-633" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span> <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>), climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Planned Completion</span><span class="st">`</span>, climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>)</span>
<span id="cb78-634"><a href="#cb78-634" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span> <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span><span class="sc">==</span><span class="dv">0</span>, climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Planned Completion</span><span class="st">`</span>, climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>)</span>
<span id="cb78-635"><a href="#cb78-635" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-636"><a href="#cb78-636" aria-hidden="true" tabindex="-1"></a><span class="co"># keep if year of agreement is identified </span></span>
<span id="cb78-637"><a href="#cb78-637" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects[<span class="sc">!</span><span class="fu">is.na</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>), ]</span>
<span id="cb78-638"><a href="#cb78-638" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-639"><a href="#cb78-639" aria-hidden="true" tabindex="-1"></a><span class="co"># drop if year is marked as &quot;0&quot; (coding error?)</span></span>
<span id="cb78-640"><a href="#cb78-640" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span><span class="fu">subset</span>(climate_projects,<span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span><span class="sc">!=</span><span class="dv">0</span>)</span>
<span id="cb78-641"><a href="#cb78-641" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-642"><a href="#cb78-642" aria-hidden="true" tabindex="-1"></a><span class="co"># amount in millins US $</span></span>
<span id="cb78-643"><a href="#cb78-643" aria-hidden="true" tabindex="-1"></a>climate_projects[<span class="fu">c</span>(<span class="st">&quot;Cumulative Disbursement&quot;</span>)] <span class="ot">&lt;-</span> climate_projects[<span class="fu">c</span>(<span class="st">&quot;Cumulative Disbursement&quot;</span>)]<span class="sc">/</span><span class="fl">1e6</span></span>
<span id="cb78-644"><a href="#cb78-644" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-645"><a href="#cb78-645" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-646"><a href="#cb78-646" aria-hidden="true" tabindex="-1"></a><span class="co"># standardize date</span></span>
<span id="cb78-647"><a href="#cb78-647" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects <span class="sc">%&gt;%</span> </span>
<span id="cb78-648"><a href="#cb78-648" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">as.character</span>(<span class="st">`</span><span class="at">Date of Agreement Signed</span><span class="st">`</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-649"><a href="#cb78-649" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/03/2006&#39;</span>, <span class="st">&#39;2006&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-650"><a href="#cb78-650" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/05/2009&#39;</span>, <span class="st">&#39;2009&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-651"><a href="#cb78-651" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/1999&#39;</span>, <span class="st">&#39;1999&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-652"><a href="#cb78-652" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2001&#39;</span>, <span class="st">&#39;2001&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-653"><a href="#cb78-653" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2002&#39;</span>, <span class="st">&#39;2002&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-654"><a href="#cb78-654" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2003&#39;</span>, <span class="st">&#39;2003&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-655"><a href="#cb78-655" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2006&#39;</span>, <span class="st">&#39;2006&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-656"><a href="#cb78-656" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-657"><a href="#cb78-657" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/07/2010&#39;</span>, <span class="st">&#39;2010&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-658"><a href="#cb78-658" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/08/2005&#39;</span>, <span class="st">&#39;2005&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-659"><a href="#cb78-659" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/09/2007&#39;</span>, <span class="st">&#39;2007&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-660"><a href="#cb78-660" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/09/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-661"><a href="#cb78-661" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/10/2004&#39;</span>, <span class="st">&#39;2004&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-662"><a href="#cb78-662" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/11/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-663"><a href="#cb78-663" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/11/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-664"><a href="#cb78-664" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/12/2007&#39;</span>, <span class="st">&#39;2007&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-665"><a href="#cb78-665" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;01/12/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-666"><a href="#cb78-666" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;02/01/2001&#39;</span>, <span class="st">&#39;2001&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-667"><a href="#cb78-667" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;02/01/2003&#39;</span>, <span class="st">&#39;2003&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-668"><a href="#cb78-668" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;11/04/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-669"><a href="#cb78-669" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;12/09/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-670"><a href="#cb78-670" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;12/09/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-671"><a href="#cb78-671" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;15/12/2006&#39;</span>, <span class="st">&#39;2006&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-672"><a href="#cb78-672" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;23/01/2007&#39;</span>, <span class="st">&#39;2007&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-673"><a href="#cb78-673" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;19/11/2008&#39;</span>, <span class="st">&#39;2008&#39;</span>))<span class="sc">%&gt;%</span> </span>
<span id="cb78-674"><a href="#cb78-674" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;05/05/2010&#39;</span>, <span class="st">&#39;2010&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-675"><a href="#cb78-675" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;31/12/2009&#39;</span>, <span class="st">&#39;2009&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-676"><a href="#cb78-676" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;31/03/2011&#39;</span>, <span class="st">&#39;2011&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-677"><a href="#cb78-677" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;31/03/2010&#39;</span>, <span class="st">&#39;2010&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-678"><a href="#cb78-678" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;30/08/2013&#39;</span>, <span class="st">&#39;2013&#39;</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-679"><a href="#cb78-679" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Year =</span> <span class="fu">replace</span>(Year, Year <span class="sc">==</span> <span class="st">&#39;30/06/2009&#39;</span>, <span class="st">&#39;2009&#39;</span>))</span>
<span id="cb78-680"><a href="#cb78-680" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-681"><a href="#cb78-681" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-682"><a href="#cb78-682" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-683"><a href="#cb78-683" aria-hidden="true" tabindex="-1"></a><span class="co"># only keep unique</span></span>
<span id="cb78-684"><a href="#cb78-684" aria-hidden="true" tabindex="-1"></a><span class="co"># some monyy targets to many places</span></span>
<span id="cb78-685"><a href="#cb78-685" aria-hidden="true" tabindex="-1"></a><span class="co"># Remove duplicates based on Sepal.Width columns</span></span>
<span id="cb78-686"><a href="#cb78-686" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects[<span class="sc">!</span><span class="fu">duplicated</span>(climate_projects<span class="sc">$</span><span class="st">`</span><span class="at">Cumulative Disbursement</span><span class="st">`</span>), ]</span>
<span id="cb78-687"><a href="#cb78-687" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-688"><a href="#cb78-688" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-689"><a href="#cb78-689" aria-hidden="true" tabindex="-1"></a>climate_projects<span class="ot">&lt;-</span>climate_projects<span class="sc">%&gt;%</span> </span>
<span id="cb78-690"><a href="#cb78-690" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(<span class="st">`</span><span class="at">Year</span><span class="st">`</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb78-691"><a href="#cb78-691" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_at</span>(<span class="fu">vars</span>(<span class="st">`</span><span class="at">Cumulative Disbursement</span><span class="st">`</span> ), <span class="fu">funs</span>(sum))</span>
<span id="cb78-692"><a href="#cb78-692" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-693"><a href="#cb78-693" aria-hidden="true" tabindex="-1"></a><span class="co"># Merge Prevention and Relief Aid Spending </span></span>
<span id="cb78-694"><a href="#cb78-694" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-695"><a href="#cb78-695" aria-hidden="true" tabindex="-1"></a><span class="co"># INNER JOIN: returns rows when there is a match in both tables.</span></span>
<span id="cb78-696"><a href="#cb78-696" aria-hidden="true" tabindex="-1"></a>prevention_response<span class="ot">&lt;-</span><span class="fu">merge</span>(climate_projects, response, <span class="at">all.y=</span><span class="cn">TRUE</span>)</span>
<span id="cb78-697"><a href="#cb78-697" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-698"><a href="#cb78-698" aria-hidden="true" tabindex="-1"></a><span class="co"># rename</span></span>
<span id="cb78-699"><a href="#cb78-699" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(prevention_response)[<span class="fu">names</span>(prevention_response) <span class="sc">==</span> <span class="st">&quot;Cumulative Disbursement&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Prevention&quot;</span></span>
<span id="cb78-700"><a href="#cb78-700" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(prevention_response)[<span class="fu">names</span>(prevention_response) <span class="sc">==</span> <span class="st">&quot;Relief_million&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Relief&quot;</span></span>
<span id="cb78-701"><a href="#cb78-701" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-702"><a href="#cb78-702" aria-hidden="true" tabindex="-1"></a><span class="co"># melt </span></span>
<span id="cb78-703"><a href="#cb78-703" aria-hidden="true" tabindex="-1"></a>prevention_response_final<span class="ot">&lt;-</span><span class="fu">melt</span>(<span class="at">data =</span> prevention_response, <span class="at">id.vars =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">measure.vars =</span> <span class="fu">c</span>(<span class="st">&quot;Prevention&quot;</span>, <span class="st">&quot;Relief&quot;</span>))</span>
<span id="cb78-704"><a href="#cb78-704" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-705"><a href="#cb78-705" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-706"><a href="#cb78-706" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb78-707"><a href="#cb78-707" aria-hidden="true" tabindex="-1"></a>fig_spending<span class="ot">&lt;-</span><span class="fu">ggplot</span>() <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">y =</span> value, <span class="at">x =</span><span class="fu">as.numeric</span>(Year), <span class="at">fill =</span> variable),</span>
<span id="cb78-708"><a href="#cb78-708" aria-hidden="true" tabindex="-1"></a>                        <span class="at">data =</span> prevention_response_final,</span>
<span id="cb78-709"><a href="#cb78-709" aria-hidden="true" tabindex="-1"></a>                        <span class="at">stat=</span><span class="st">&quot;identity&quot;</span>)<span class="sc">+</span> </span>
<span id="cb78-710"><a href="#cb78-710" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">16</span>) <span class="sc">+</span></span>
<span id="cb78-711"><a href="#cb78-711" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_grey</span>()<span class="sc">+</span></span>
<span id="cb78-712"><a href="#cb78-712" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-713"><a href="#cb78-713" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">y =</span><span class="st">&quot;Total Amount in Million US$&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-714"><a href="#cb78-714" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Prevention &amp; Relief Spending&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-715"><a href="#cb78-715" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">title=</span><span class="st">&quot; &quot;</span>))</span>
<span id="cb78-716"><a href="#cb78-716" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-717"><a href="#cb78-717" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-718"><a href="#cb78-718" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-719"><a href="#cb78-719" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-720"><a href="#cb78-720" aria-hidden="true" tabindex="-1"></a><span class="co"># Responsabilities</span></span>
<span id="cb78-721"><a href="#cb78-721" aria-hidden="true" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;1_input/covariates.rds&quot;</span>)</span>
<span id="cb78-722"><a href="#cb78-722" aria-hidden="true" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> covariates <span class="sc">%&gt;%</span></span>
<span id="cb78-723"><a href="#cb78-723" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(</span>
<span id="cb78-724"><a href="#cb78-724" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_ta&quot;</span> <span class="ot">=</span> T_q38_1,</span>
<span id="cb78-725"><a href="#cb78-725" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_village_head&quot;</span> <span class="ot">=</span> T_q38_2,</span>
<span id="cb78-726"><a href="#cb78-726" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_relig_leader&quot;</span> <span class="ot">=</span> T_q38_3,</span>
<span id="cb78-727"><a href="#cb78-727" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_mp&quot;</span> <span class="ot">=</span> T_q38_4,</span>
<span id="cb78-728"><a href="#cb78-728" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_local_council&quot;</span> <span class="ot">=</span> T_q38_5,</span>
<span id="cb78-729"><a href="#cb78-729" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_district_commissioner&quot;</span> <span class="ot">=</span> T_q38_7,</span>
<span id="cb78-730"><a href="#cb78-730" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_ngo_io&quot;</span> <span class="ot">=</span> T_q38_8,</span>
<span id="cb78-731"><a href="#cb78-731" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_prepar_dodma&quot;</span> <span class="ot">=</span> T_q38_9,</span>
<span id="cb78-732"><a href="#cb78-732" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_ta&quot;</span> <span class="ot">=</span> T_q37_1,</span>
<span id="cb78-733"><a href="#cb78-733" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_village_head&quot;</span> <span class="ot">=</span> T_q37_2,</span>
<span id="cb78-734"><a href="#cb78-734" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_relig_leader&quot;</span> <span class="ot">=</span> T_q37_3,</span>
<span id="cb78-735"><a href="#cb78-735" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_mp&quot;</span> <span class="ot">=</span> T_q37_4,</span>
<span id="cb78-736"><a href="#cb78-736" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_local_council&quot;</span> <span class="ot">=</span> T_q37_5,</span>
<span id="cb78-737"><a href="#cb78-737" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_district_commissioner&quot;</span> <span class="ot">=</span> T_q37_7,</span>
<span id="cb78-738"><a href="#cb78-738" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_ngo_io&quot;</span> <span class="ot">=</span> T_q37_8,</span>
<span id="cb78-739"><a href="#cb78-739" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;resp_relief_dodma&quot;</span> <span class="ot">=</span> T_q37_9</span>
<span id="cb78-740"><a href="#cb78-740" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-741"><a href="#cb78-741" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-742"><a href="#cb78-742" aria-hidden="true" tabindex="-1"></a><span class="co"># List of the renamed variables for preparation and relief</span></span>
<span id="cb78-743"><a href="#cb78-743" aria-hidden="true" tabindex="-1"></a>prep_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;resp_prepar_ta&quot;</span>, <span class="st">&quot;resp_prepar_village_head&quot;</span>, <span class="st">&quot;resp_prepar_relig_leader&quot;</span>, </span>
<span id="cb78-744"><a href="#cb78-744" aria-hidden="true" tabindex="-1"></a>               <span class="st">&quot;resp_prepar_mp&quot;</span>, <span class="st">&quot;resp_prepar_local_council&quot;</span>, <span class="st">&quot;resp_prepar_district_commissioner&quot;</span>, </span>
<span id="cb78-745"><a href="#cb78-745" aria-hidden="true" tabindex="-1"></a>               <span class="st">&quot;resp_prepar_ngo_io&quot;</span>, <span class="st">&quot;resp_prepar_dodma&quot;</span>)</span>
<span id="cb78-746"><a href="#cb78-746" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-747"><a href="#cb78-747" aria-hidden="true" tabindex="-1"></a>relief_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;resp_relief_ta&quot;</span>, <span class="st">&quot;resp_relief_village_head&quot;</span>, <span class="st">&quot;resp_relief_relig_leader&quot;</span>, </span>
<span id="cb78-748"><a href="#cb78-748" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&quot;resp_relief_mp&quot;</span>, <span class="st">&quot;resp_relief_local_council&quot;</span>, <span class="st">&quot;resp_relief_district_commissioner&quot;</span>, </span>
<span id="cb78-749"><a href="#cb78-749" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&quot;resp_relief_ngo_io&quot;</span>, <span class="st">&quot;resp_relief_dodma&quot;</span>)</span>
<span id="cb78-750"><a href="#cb78-750" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-751"><a href="#cb78-751" aria-hidden="true" tabindex="-1"></a><span class="co"># Function to count the number of &quot;3&quot; responses for a given set of variables</span></span>
<span id="cb78-752"><a href="#cb78-752" aria-hidden="true" tabindex="-1"></a>count_responsibility <span class="ot">&lt;-</span> <span class="cf">function</span>(df, variables) {</span>
<span id="cb78-753"><a href="#cb78-753" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Ensure that the variables are numeric and count the &quot;3&quot; responses</span></span>
<span id="cb78-754"><a href="#cb78-754" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sapply</span>(variables, <span class="cf">function</span>(var) <span class="fu">sum</span>(<span class="fu">as.numeric</span>(df[[var]]) <span class="sc">==</span> <span class="dv">3</span>, <span class="at">na.rm =</span> <span class="cn">TRUE</span>))</span>
<span id="cb78-755"><a href="#cb78-755" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb78-756"><a href="#cb78-756" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-757"><a href="#cb78-757" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-758"><a href="#cb78-758" aria-hidden="true" tabindex="-1"></a><span class="co"># Count &quot;3&quot; responses for preparation and relief</span></span>
<span id="cb78-759"><a href="#cb78-759" aria-hidden="true" tabindex="-1"></a>Prevention <span class="ot">&lt;-</span> <span class="fu">count_responsibility</span>(covariates, prep_vars)</span>
<span id="cb78-760"><a href="#cb78-760" aria-hidden="true" tabindex="-1"></a>Relief <span class="ot">&lt;-</span> <span class="fu">count_responsibility</span>(covariates, relief_vars)</span>
<span id="cb78-761"><a href="#cb78-761" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-762"><a href="#cb78-762" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a dataframe for plotting</span></span>
<span id="cb78-763"><a href="#cb78-763" aria-hidden="true" tabindex="-1"></a>Office <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;TA&quot;</span>, <span class="st">&quot;Village </span><span class="sc">\n</span><span class="st"> Head&quot;</span>, <span class="st">&quot;Relig. </span><span class="sc">\n</span><span class="st"> Leader&quot;</span>, <span class="st">&quot;MP&quot;</span>, <span class="st">&quot;Council&quot;</span>, </span>
<span id="cb78-764"><a href="#cb78-764" aria-hidden="true" tabindex="-1"></a>            <span class="st">&quot;District&quot;</span>, <span class="st">&quot;IO </span><span class="sc">\n</span><span class="st"> NGO&quot;</span>, <span class="st">&quot;DoDMA&quot;</span>)</span>
<span id="cb78-765"><a href="#cb78-765" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the counts into a single dataframe</span></span>
<span id="cb78-766"><a href="#cb78-766" aria-hidden="true" tabindex="-1"></a>response <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(Office, Prevention, Relief)</span>
<span id="cb78-767"><a href="#cb78-767" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-768"><a href="#cb78-768" aria-hidden="true" tabindex="-1"></a><span class="co"># Reshape the data for plotting (long format)</span></span>
<span id="cb78-769"><a href="#cb78-769" aria-hidden="true" tabindex="-1"></a>response_long <span class="ot">&lt;-</span> response <span class="sc">%&gt;%</span></span>
<span id="cb78-770"><a href="#cb78-770" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="at">cols =</span> <span class="fu">c</span>(<span class="st">&quot;Prevention&quot;</span>, <span class="st">&quot;Relief&quot;</span>), <span class="at">names_to =</span> <span class="st">&quot;Category&quot;</span>, <span class="at">values_to =</span> <span class="st">&quot;Count&quot;</span>)</span>
<span id="cb78-771"><a href="#cb78-771" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-772"><a href="#cb78-772" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the data</span></span>
<span id="cb78-773"><a href="#cb78-773" aria-hidden="true" tabindex="-1"></a>responsability<span class="ot">&lt;-</span><span class="fu">ggplot</span>(response_long, <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">reorder</span>(Office, <span class="sc">-</span>Count), <span class="at">y =</span> Count, <span class="at">fill =</span> Category)) <span class="sc">+</span></span>
<span id="cb78-774"><a href="#cb78-774" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-775"><a href="#cb78-775" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_grey</span>()<span class="sc">+</span></span>
<span id="cb78-776"><a href="#cb78-776" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">16</span>) <span class="sc">+</span></span>
<span id="cb78-777"><a href="#cb78-777" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-778"><a href="#cb78-778" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">y =</span><span class="st">&quot;Total Number of Responses&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-779"><a href="#cb78-779" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Prevention &amp; Relief Responsibilities Malawi&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-780"><a href="#cb78-780" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">title=</span><span class="st">&quot; &quot;</span>))</span>
<span id="cb78-781"><a href="#cb78-781" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-782"><a href="#cb78-782" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-783"><a href="#cb78-783" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-784"><a href="#cb78-784" aria-hidden="true" tabindex="-1"></a>fig_A3<span class="ot">&lt;-</span><span class="fu">ggarrange</span>(</span>
<span id="cb78-785"><a href="#cb78-785" aria-hidden="true" tabindex="-1"></a>  fig_spending, responsability,</span>
<span id="cb78-786"><a href="#cb78-786" aria-hidden="true" tabindex="-1"></a>  <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend =</span> <span class="st">&quot;bottom&quot;</span></span>
<span id="cb78-787"><a href="#cb78-787" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-788"><a href="#cb78-788" aria-hidden="true" tabindex="-1"></a>fig_A3</span>
<span id="cb78-789"><a href="#cb78-789" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-790"><a href="#cb78-790" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A3_malawi.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-791"><a href="#cb78-791" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-792"><a href="#cb78-792" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-793"><a href="#cb78-793" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-794"><a href="#cb78-794" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-795"><a href="#cb78-795" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A5: Help received after the 2015 flood (left) and satisfaction with response (right)</span></span>
<span id="cb78-796"><a href="#cb78-796" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-797"><a href="#cb78-797" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , echo=TRUE,warning=FALSE}</span></span>
<span id="cb78-798"><a href="#cb78-798" aria-hidden="true" tabindex="-1"></a>covariates<span class="ot">&lt;-</span><span class="fu">readRDS</span>(<span class="at">file =</span> <span class="st">&quot;1_input/covariates.rds&quot;</span>)</span>
<span id="cb78-799"><a href="#cb78-799" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-800"><a href="#cb78-800" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-801"><a href="#cb78-801" aria-hidden="true" tabindex="-1"></a>covariates<span class="ot">&lt;-</span>covariates <span class="sc">%&gt;%</span> </span>
<span id="cb78-802"><a href="#cb78-802" aria-hidden="true" tabindex="-1"></a>    <span class="fu">drop_na</span>(satisfied_personal_help) <span class="sc">%&gt;%</span></span>
<span id="cb78-803"><a href="#cb78-803" aria-hidden="true" tabindex="-1"></a>     dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-804"><a href="#cb78-804" aria-hidden="true" tabindex="-1"></a>       <span class="at">personal_help =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-805"><a href="#cb78-805" aria-hidden="true" tabindex="-1"></a>              personal_help <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No&quot;</span>,</span>
<span id="cb78-806"><a href="#cb78-806" aria-hidden="true" tabindex="-1"></a>              personal_help <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Yes&quot;</span></span>
<span id="cb78-807"><a href="#cb78-807" aria-hidden="true" tabindex="-1"></a>              ),</span>
<span id="cb78-808"><a href="#cb78-808" aria-hidden="true" tabindex="-1"></a>       <span class="at">community_help =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-809"><a href="#cb78-809" aria-hidden="true" tabindex="-1"></a>              community_help <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No&quot;</span>,</span>
<span id="cb78-810"><a href="#cb78-810" aria-hidden="true" tabindex="-1"></a>              community_help <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Yes&quot;</span></span>
<span id="cb78-811"><a href="#cb78-811" aria-hidden="true" tabindex="-1"></a>              ),</span>
<span id="cb78-812"><a href="#cb78-812" aria-hidden="true" tabindex="-1"></a>       <span class="at">satisfied_personal_help =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-813"><a href="#cb78-813" aria-hidden="true" tabindex="-1"></a>              satisfied_personal_help <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Very Satisfied&quot;</span>,</span>
<span id="cb78-814"><a href="#cb78-814" aria-hidden="true" tabindex="-1"></a>              satisfied_personal_help <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot; Somewhat Satisfied&quot;</span>,</span>
<span id="cb78-815"><a href="#cb78-815" aria-hidden="true" tabindex="-1"></a>              satisfied_personal_help <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;  Somewhat Dissatisfied&quot;</span>,</span>
<span id="cb78-816"><a href="#cb78-816" aria-hidden="true" tabindex="-1"></a>              satisfied_personal_help <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;   Very Dissatisfied&quot;</span>,</span>
<span id="cb78-817"><a href="#cb78-817" aria-hidden="true" tabindex="-1"></a>                         <span class="cn">TRUE</span> <span class="sc">~</span> satisfied_personal_help),</span>
<span id="cb78-818"><a href="#cb78-818" aria-hidden="true" tabindex="-1"></a>      <span class="at">community_help_id1 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-819"><a href="#cb78-819" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Traditional authority&quot;</span>,</span>
<span id="cb78-820"><a href="#cb78-820" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Village head&quot;</span>,</span>
<span id="cb78-821"><a href="#cb78-821" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Religious leader&quot;</span>,</span>
<span id="cb78-822"><a href="#cb78-822" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb78-823"><a href="#cb78-823" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Local council member&quot;</span>,</span>
<span id="cb78-824"><a href="#cb78-824" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;District commissioner&quot;</span>,</span>
<span id="cb78-825"><a href="#cb78-825" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb78-826"><a href="#cb78-826" aria-hidden="true" tabindex="-1"></a>              community_help_id1 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;Dodma&quot;</span>,</span>
<span id="cb78-827"><a href="#cb78-827" aria-hidden="true" tabindex="-1"></a>              <span class="cn">TRUE</span> <span class="sc">~</span> community_help_id1),</span>
<span id="cb78-828"><a href="#cb78-828" aria-hidden="true" tabindex="-1"></a>      <span class="at">community_help_id2 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-829"><a href="#cb78-829" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Traditional authority&quot;</span>,</span>
<span id="cb78-830"><a href="#cb78-830" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Village head&quot;</span>,</span>
<span id="cb78-831"><a href="#cb78-831" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Religious leader&quot;</span>,</span>
<span id="cb78-832"><a href="#cb78-832" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb78-833"><a href="#cb78-833" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Local council member&quot;</span>,</span>
<span id="cb78-834"><a href="#cb78-834" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;District commissioner&quot;</span>,</span>
<span id="cb78-835"><a href="#cb78-835" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb78-836"><a href="#cb78-836" aria-hidden="true" tabindex="-1"></a>              community_help_id2 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;Dodma&quot;</span>,</span>
<span id="cb78-837"><a href="#cb78-837" aria-hidden="true" tabindex="-1"></a>              <span class="cn">TRUE</span> <span class="sc">~</span> community_help_id2),</span>
<span id="cb78-838"><a href="#cb78-838" aria-hidden="true" tabindex="-1"></a>      <span class="at">community_help_id3 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-839"><a href="#cb78-839" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Traditional authority&quot;</span>,</span>
<span id="cb78-840"><a href="#cb78-840" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Village head&quot;</span>,</span>
<span id="cb78-841"><a href="#cb78-841" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Religious leader&quot;</span>,</span>
<span id="cb78-842"><a href="#cb78-842" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb78-843"><a href="#cb78-843" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Local council member&quot;</span>,</span>
<span id="cb78-844"><a href="#cb78-844" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;District commissioner&quot;</span>,</span>
<span id="cb78-845"><a href="#cb78-845" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb78-846"><a href="#cb78-846" aria-hidden="true" tabindex="-1"></a>              community_help_id3 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;Dodma&quot;</span>,</span>
<span id="cb78-847"><a href="#cb78-847" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> community_help_id3),</span>
<span id="cb78-848"><a href="#cb78-848" aria-hidden="true" tabindex="-1"></a>      <span class="at">personal_help_id1 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-849"><a href="#cb78-849" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Family&quot;</span>,</span>
<span id="cb78-850"><a href="#cb78-850" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Friends&quot;</span>,</span>
<span id="cb78-851"><a href="#cb78-851" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Traditional Authority&quot;</span>,</span>
<span id="cb78-852"><a href="#cb78-852" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Village Head&quot;</span>,</span>
<span id="cb78-853"><a href="#cb78-853" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Religious Leader&quot;</span>,</span>
<span id="cb78-854"><a href="#cb78-854" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb78-855"><a href="#cb78-855" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;Local Council&quot;</span>,</span>
<span id="cb78-856"><a href="#cb78-856" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;District Commissioner&quot;</span>,</span>
<span id="cb78-857"><a href="#cb78-857" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">9</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb78-858"><a href="#cb78-858" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">10</span> <span class="sc">~</span> <span class="st">&quot;Department of Disaster Management&quot;</span>,</span>
<span id="cb78-859"><a href="#cb78-859" aria-hidden="true" tabindex="-1"></a>              personal_help_id1 <span class="sc">==</span> <span class="dv">11</span> <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-860"><a href="#cb78-860" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> personal_help_id1),</span>
<span id="cb78-861"><a href="#cb78-861" aria-hidden="true" tabindex="-1"></a>      <span class="at">personal_help_id2 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-862"><a href="#cb78-862" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Family&quot;</span>,</span>
<span id="cb78-863"><a href="#cb78-863" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Friends&quot;</span>,</span>
<span id="cb78-864"><a href="#cb78-864" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Traditional Authority&quot;</span>,</span>
<span id="cb78-865"><a href="#cb78-865" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Village Head&quot;</span>,</span>
<span id="cb78-866"><a href="#cb78-866" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Religious Leader&quot;</span>,</span>
<span id="cb78-867"><a href="#cb78-867" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb78-868"><a href="#cb78-868" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;Local Council&quot;</span>,</span>
<span id="cb78-869"><a href="#cb78-869" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;District Commissioner&quot;</span>,</span>
<span id="cb78-870"><a href="#cb78-870" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">9</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb78-871"><a href="#cb78-871" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">10</span> <span class="sc">~</span> <span class="st">&quot;Department of Disaster Management&quot;</span>,</span>
<span id="cb78-872"><a href="#cb78-872" aria-hidden="true" tabindex="-1"></a>              personal_help_id2 <span class="sc">==</span> <span class="dv">11</span> <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-873"><a href="#cb78-873" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> personal_help_id2),</span>
<span id="cb78-874"><a href="#cb78-874" aria-hidden="true" tabindex="-1"></a>      <span class="at">personal_help_id3 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-875"><a href="#cb78-875" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Family&quot;</span>,</span>
<span id="cb78-876"><a href="#cb78-876" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Friends&quot;</span>,</span>
<span id="cb78-877"><a href="#cb78-877" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Traditional Authority&quot;</span>,</span>
<span id="cb78-878"><a href="#cb78-878" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Village Head&quot;</span>,</span>
<span id="cb78-879"><a href="#cb78-879" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Religious Leader&quot;</span>,</span>
<span id="cb78-880"><a href="#cb78-880" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb78-881"><a href="#cb78-881" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;Local Council&quot;</span>,</span>
<span id="cb78-882"><a href="#cb78-882" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;District Commissioner&quot;</span>,</span>
<span id="cb78-883"><a href="#cb78-883" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">9</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb78-884"><a href="#cb78-884" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">10</span> <span class="sc">~</span> <span class="st">&quot;Department of Disaster Management&quot;</span>,</span>
<span id="cb78-885"><a href="#cb78-885" aria-hidden="true" tabindex="-1"></a>              personal_help_id3 <span class="sc">==</span> <span class="dv">11</span> <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-886"><a href="#cb78-886" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> personal_help_id3),</span>
<span id="cb78-887"><a href="#cb78-887" aria-hidden="true" tabindex="-1"></a>      <span class="at">personal_help_id4 =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb78-888"><a href="#cb78-888" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Family&quot;</span>,</span>
<span id="cb78-889"><a href="#cb78-889" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Friends&quot;</span>,</span>
<span id="cb78-890"><a href="#cb78-890" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Traditional Authority&quot;</span>,</span>
<span id="cb78-891"><a href="#cb78-891" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Village Head&quot;</span>,</span>
<span id="cb78-892"><a href="#cb78-892" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Religious Leader&quot;</span>,</span>
<span id="cb78-893"><a href="#cb78-893" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">6</span> <span class="sc">~</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb78-894"><a href="#cb78-894" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&quot;Local Council&quot;</span>,</span>
<span id="cb78-895"><a href="#cb78-895" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">8</span> <span class="sc">~</span> <span class="st">&quot;District Commissioner&quot;</span>,</span>
<span id="cb78-896"><a href="#cb78-896" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">9</span> <span class="sc">~</span> <span class="st">&quot;NGO, International Organization&quot;</span>,</span>
<span id="cb78-897"><a href="#cb78-897" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">10</span> <span class="sc">~</span> <span class="st">&quot;Department of Disaster Management&quot;</span>,</span>
<span id="cb78-898"><a href="#cb78-898" aria-hidden="true" tabindex="-1"></a>              personal_help_id4 <span class="sc">==</span> <span class="dv">11</span> <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-899"><a href="#cb78-899" aria-hidden="true" tabindex="-1"></a>                <span class="cn">TRUE</span> <span class="sc">~</span> personal_help_id4)</span>
<span id="cb78-900"><a href="#cb78-900" aria-hidden="true" tabindex="-1"></a>      ) </span>
<span id="cb78-901"><a href="#cb78-901" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-902"><a href="#cb78-902" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-903"><a href="#cb78-903" aria-hidden="true" tabindex="-1"></a>personal_help<span class="ot">&lt;-</span>covariates <span class="sc">%&gt;%</span></span>
<span id="cb78-904"><a href="#cb78-904" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">count</span>(personal_help) <span class="sc">%&gt;%</span></span>
<span id="cb78-905"><a href="#cb78-905" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">sum_group =</span> n) <span class="sc">%&gt;%</span></span>
<span id="cb78-906"><a href="#cb78-906" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb78-907"><a href="#cb78-907" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">x=</span>personal_help,<span class="at">y =</span> (sum_group)<span class="sc">/</span><span class="fu">sum</span>(sum_group)),<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>,<span class="at">width=</span><span class="fl">0.4</span>) <span class="sc">+</span></span>
<span id="cb78-908"><a href="#cb78-908" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">labels=</span>scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb78-909"><a href="#cb78-909" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Density&#39;</span>,<span class="at">x=</span><span class="st">&#39;After the 2015 floods, did you personally receive help?&#39;</span>)<span class="sc">+</span></span>
<span id="cb78-910"><a href="#cb78-910" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
<span id="cb78-911"><a href="#cb78-911" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-912"><a href="#cb78-912" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-913"><a href="#cb78-913" aria-hidden="true" tabindex="-1"></a>satisfied_personal_help<span class="ot">&lt;-</span>covariates <span class="sc">%&gt;%</span></span>
<span id="cb78-914"><a href="#cb78-914" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(satisfied_personal_help<span class="sc">!=</span><span class="st">&quot;.&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-915"><a href="#cb78-915" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">count</span>(satisfied_personal_help) <span class="sc">%&gt;%</span></span>
<span id="cb78-916"><a href="#cb78-916" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">sum_group =</span> n) <span class="sc">%&gt;%</span></span>
<span id="cb78-917"><a href="#cb78-917" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb78-918"><a href="#cb78-918" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">x=</span>satisfied_personal_help,<span class="at">y =</span> (sum_group)<span class="sc">/</span><span class="fu">sum</span>(sum_group)),<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>,<span class="at">width=</span><span class="fl">0.4</span>) <span class="sc">+</span></span>
<span id="cb78-919"><a href="#cb78-919" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">labels=</span>scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb78-920"><a href="#cb78-920" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Density&#39;</span>,<span class="at">x=</span><span class="st">&#39;How satisfied were you with the help you received?&#39;</span>)<span class="sc">+</span></span>
<span id="cb78-921"><a href="#cb78-921" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
<span id="cb78-922"><a href="#cb78-922" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-923"><a href="#cb78-923" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-924"><a href="#cb78-924" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-925"><a href="#cb78-925" aria-hidden="true" tabindex="-1"></a>help_rec <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(personal_help,satisfied_personal_help,</span>
<span id="cb78-926"><a href="#cb78-926" aria-hidden="true" tabindex="-1"></a>                    <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>)</span>
<span id="cb78-927"><a href="#cb78-927" aria-hidden="true" tabindex="-1"></a>help_rec</span>
<span id="cb78-928"><a href="#cb78-928" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-929"><a href="#cb78-929" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A5_help_rec.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-930"><a href="#cb78-930" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-931"><a href="#cb78-931" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-932"><a href="#cb78-932" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-933"><a href="#cb78-933" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-934"><a href="#cb78-934" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A6: Actors help received (left) and satisfaction when received help only from MP (right)</span></span>
<span id="cb78-935"><a href="#cb78-935" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-936"><a href="#cb78-936" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , echo=TRUE,warning=FALSE}</span></span>
<span id="cb78-937"><a href="#cb78-937" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-938"><a href="#cb78-938" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-939"><a href="#cb78-939" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-940"><a href="#cb78-940" aria-hidden="true" tabindex="-1"></a><span class="co"># [q31/Q_48_S] From whom did you receive help? (only personal)</span></span>
<span id="cb78-941"><a href="#cb78-941" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-942"><a href="#cb78-942" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span>
<span id="cb78-943"><a href="#cb78-943" aria-hidden="true" tabindex="-1"></a>personal_help_id1<span class="ot">&lt;-</span><span class="fu">as.data.frame</span>(covariates<span class="sc">$</span>personal_help_id1)</span>
<span id="cb78-944"><a href="#cb78-944" aria-hidden="true" tabindex="-1"></a>personal_help_id2<span class="ot">&lt;-</span><span class="fu">as.data.frame</span>(covariates<span class="sc">$</span>personal_help_id2)</span>
<span id="cb78-945"><a href="#cb78-945" aria-hidden="true" tabindex="-1"></a>personal_help_id3<span class="ot">&lt;-</span><span class="fu">as.data.frame</span>(covariates<span class="sc">$</span>personal_help_id3)</span>
<span id="cb78-946"><a href="#cb78-946" aria-hidden="true" tabindex="-1"></a>personal_help_id4<span class="ot">&lt;-</span><span class="fu">as.data.frame</span>(covariates<span class="sc">$</span>personal_help_id4)</span>
<span id="cb78-947"><a href="#cb78-947" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-948"><a href="#cb78-948" aria-hidden="true" tabindex="-1"></a><span class="co"># drop all missing</span></span>
<span id="cb78-949"><a href="#cb78-949" aria-hidden="true" tabindex="-1"></a>personal_help_id1<span class="ot">&lt;-</span><span class="fu">subset</span>(personal_help_id1, personal_help_id1<span class="sc">!=</span><span class="st">&quot;.&quot;</span> <span class="sc">&amp;</span> personal_help_id1<span class="sc">!=</span><span class="st">&quot;NA&quot;</span>)</span>
<span id="cb78-950"><a href="#cb78-950" aria-hidden="true" tabindex="-1"></a>personal_help_id2<span class="ot">&lt;-</span><span class="fu">subset</span>(personal_help_id2, personal_help_id2<span class="sc">!=</span><span class="st">&quot;.&quot;</span> <span class="sc">&amp;</span> personal_help_id2<span class="sc">!=</span><span class="st">&quot;NA&quot;</span>)</span>
<span id="cb78-951"><a href="#cb78-951" aria-hidden="true" tabindex="-1"></a>personal_help_id3<span class="ot">&lt;-</span><span class="fu">subset</span>(personal_help_id3, personal_help_id3<span class="sc">!=</span><span class="st">&quot;.&quot;</span> <span class="sc">&amp;</span> personal_help_id3<span class="sc">!=</span><span class="st">&quot;NA&quot;</span>)</span>
<span id="cb78-952"><a href="#cb78-952" aria-hidden="true" tabindex="-1"></a>personal_help_id4<span class="ot">&lt;-</span><span class="fu">subset</span>(personal_help_id4, personal_help_id4<span class="sc">!=</span><span class="st">&quot;.&quot;</span> <span class="sc">&amp;</span> personal_help_id4<span class="sc">!=</span><span class="st">&quot;NA&quot;</span>)</span>
<span id="cb78-953"><a href="#cb78-953" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-954"><a href="#cb78-954" aria-hidden="true" tabindex="-1"></a><span class="co"># rename</span></span>
<span id="cb78-955"><a href="#cb78-955" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(personal_help_id1)[<span class="fu">names</span>(personal_help_id1) <span class="sc">==</span> <span class="st">&#39;covariates$personal_help_id1&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;q31&#39;</span></span>
<span id="cb78-956"><a href="#cb78-956" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(personal_help_id2)[<span class="fu">names</span>(personal_help_id2) <span class="sc">==</span> <span class="st">&#39;covariates$personal_help_id2&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;q31&#39;</span></span>
<span id="cb78-957"><a href="#cb78-957" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(personal_help_id3)[<span class="fu">names</span>(personal_help_id3) <span class="sc">==</span> <span class="st">&#39;covariates$personal_help_id3&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;q31&#39;</span></span>
<span id="cb78-958"><a href="#cb78-958" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(personal_help_id4)[<span class="fu">names</span>(personal_help_id4) <span class="sc">==</span> <span class="st">&#39;covariates$personal_help_id4&#39;</span>] <span class="ot">&lt;-</span> <span class="st">&#39;q31&#39;</span></span>
<span id="cb78-959"><a href="#cb78-959" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-960"><a href="#cb78-960" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-961"><a href="#cb78-961" aria-hidden="true" tabindex="-1"></a><span class="co"># append</span></span>
<span id="cb78-962"><a href="#cb78-962" aria-hidden="true" tabindex="-1"></a>total <span class="ot">&lt;-</span> <span class="fu">rbind</span>(personal_help_id1, personal_help_id2,personal_help_id3,personal_help_id4)</span>
<span id="cb78-963"><a href="#cb78-963" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-964"><a href="#cb78-964" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-965"><a href="#cb78-965" aria-hidden="true" tabindex="-1"></a><span class="co">#aggregate</span></span>
<span id="cb78-966"><a href="#cb78-966" aria-hidden="true" tabindex="-1"></a>total<span class="sc">$</span>value <span class="ot">&lt;-</span> <span class="dv">1</span></span>
<span id="cb78-967"><a href="#cb78-967" aria-hidden="true" tabindex="-1"></a>help_id<span class="ot">&lt;-</span><span class="fu">aggregate</span>(total<span class="sc">$</span>value, <span class="at">by=</span><span class="fu">list</span>(<span class="at">help=</span>total<span class="sc">$</span>q31), <span class="at">FUN=</span>sum)</span>
<span id="cb78-968"><a href="#cb78-968" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-969"><a href="#cb78-969" aria-hidden="true" tabindex="-1"></a><span class="co"># plot </span></span>
<span id="cb78-970"><a href="#cb78-970" aria-hidden="true" tabindex="-1"></a>fig_help_id<span class="ot">&lt;-</span>help_id <span class="sc">%&gt;%</span></span>
<span id="cb78-971"><a href="#cb78-971" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb78-972"><a href="#cb78-972" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">x=</span><span class="fu">reorder</span>(help, x),<span class="at">y =</span> (x)<span class="sc">/</span><span class="fu">sum</span>(x)),<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-973"><a href="#cb78-973" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_y_continuous</span>(<span class="at">labels=</span>scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb78-974"><a href="#cb78-974" aria-hidden="true" tabindex="-1"></a>    <span class="fu">coord_flip</span>() <span class="sc">+</span> </span>
<span id="cb78-975"><a href="#cb78-975" aria-hidden="true" tabindex="-1"></a>    <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Number&#39;</span>,<span class="at">x=</span><span class="st">&#39;Actor&#39;</span>)<span class="sc">+</span></span>
<span id="cb78-976"><a href="#cb78-976" aria-hidden="true" tabindex="-1"></a>    <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb78-977"><a href="#cb78-977" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggtitle</span>(<span class="st">&quot;From whom did you receive help?&quot;</span>)</span>
<span id="cb78-978"><a href="#cb78-978" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-979"><a href="#cb78-979" aria-hidden="true" tabindex="-1"></a>MP_satisfied <span class="ot">&lt;-</span> covariates <span class="sc">%&gt;%</span></span>
<span id="cb78-980"><a href="#cb78-980" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(satisfied_personal_help <span class="sc">!=</span> <span class="st">&quot;.&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-981"><a href="#cb78-981" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(personal_help_id1 <span class="sc">==</span> <span class="st">&quot;MP&quot;</span>,</span>
<span id="cb78-982"><a href="#cb78-982" aria-hidden="true" tabindex="-1"></a>                personal_help_id2 <span class="sc">==</span> <span class="st">&quot;.&quot;</span>,</span>
<span id="cb78-983"><a href="#cb78-983" aria-hidden="true" tabindex="-1"></a>                personal_help_id3 <span class="sc">==</span> <span class="st">&quot;.&quot;</span>,</span>
<span id="cb78-984"><a href="#cb78-984" aria-hidden="true" tabindex="-1"></a>                personal_help_id4 <span class="sc">==</span> <span class="st">&quot;.&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-985"><a href="#cb78-985" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">count</span>(satisfied_personal_help) <span class="sc">%&gt;%</span></span>
<span id="cb78-986"><a href="#cb78-986" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">sum_group =</span> n) <span class="sc">%&gt;%</span></span>
<span id="cb78-987"><a href="#cb78-987" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-988"><a href="#cb78-988" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">x=</span>satisfied_personal_help,<span class="at">y =</span> (sum_group)<span class="sc">/</span><span class="fu">sum</span>(sum_group)<span class="sc">*</span><span class="dv">100</span>),<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>,<span class="at">width=</span><span class="fl">0.4</span>) <span class="sc">+</span></span>
<span id="cb78-989"><a href="#cb78-989" aria-hidden="true" tabindex="-1"></a>    <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb78-990"><a href="#cb78-990" aria-hidden="true" tabindex="-1"></a>    <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Number of respondents in %&#39;</span>,<span class="at">x=</span><span class="st">&#39;How satisfied were you with the help you received?&#39;</span>)<span class="sc">+</span></span>
<span id="cb78-991"><a href="#cb78-991" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggtitle</span>(<span class="st">&quot;Subset: Respondents who recieved help only from MP&quot;</span>)</span>
<span id="cb78-992"><a href="#cb78-992" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-993"><a href="#cb78-993" aria-hidden="true" tabindex="-1"></a>help_id <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(fig_help_id,MP_satisfied,</span>
<span id="cb78-994"><a href="#cb78-994" aria-hidden="true" tabindex="-1"></a>                    <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>)</span>
<span id="cb78-995"><a href="#cb78-995" aria-hidden="true" tabindex="-1"></a>help_id</span>
<span id="cb78-996"><a href="#cb78-996" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-997"><a href="#cb78-997" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A6_help_id.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-998"><a href="#cb78-998" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-999"><a href="#cb78-999" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1000"><a href="#cb78-1000" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1001"><a href="#cb78-1001" aria-hidden="true" tabindex="-1"></a><span class="fu">## Table A1: Summary Statistics Conjoint Experiment</span></span>
<span id="cb78-1002"><a href="#cb78-1002" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1003"><a href="#cb78-1003" aria-hidden="true" tabindex="-1"></a><span class="in">```{r summary_stats_table1, results=&quot;asis&quot;, message=FALSE, echo=FALSE,warning=FALSE}</span></span>
<span id="cb78-1004"><a href="#cb78-1004" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1005"><a href="#cb78-1005" aria-hidden="true" tabindex="-1"></a><span class="do">######################################</span></span>
<span id="cb78-1006"><a href="#cb78-1006" aria-hidden="true" tabindex="-1"></a><span class="co"># summary Statistics</span></span>
<span id="cb78-1007"><a href="#cb78-1007" aria-hidden="true" tabindex="-1"></a><span class="do">######################################</span></span>
<span id="cb78-1008"><a href="#cb78-1008" aria-hidden="true" tabindex="-1"></a>df_long <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;1_input/conjoint.rds&quot;</span>)</span>
<span id="cb78-1009"><a href="#cb78-1009" aria-hidden="true" tabindex="-1"></a><span class="fu">st</span>(df_long)</span>
<span id="cb78-1010"><a href="#cb78-1010" aria-hidden="true" tabindex="-1"></a><span class="fu">st</span>(df_long, <span class="at">out =</span> <span class="st">&#39;latex&#39;</span>, <span class="at">file =</span> <span class="st">&quot;2_output/tab_A1.tex&quot;</span>)</span>
<span id="cb78-1011"><a href="#cb78-1011" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1012"><a href="#cb78-1012" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1013"><a href="#cb78-1013" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1014"><a href="#cb78-1014" aria-hidden="true" tabindex="-1"></a><span class="fu">## Table A2: Summary Statistics</span></span>
<span id="cb78-1015"><a href="#cb78-1015" aria-hidden="true" tabindex="-1"></a><span class="in">```{r summary_stats_table2, results=&quot;asis&quot;, message=FALSE, echo=FALSE,warning=FALSE}</span></span>
<span id="cb78-1016"><a href="#cb78-1016" aria-hidden="true" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;1_input/covariates.rds&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-1017"><a href="#cb78-1017" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="sc">-</span><span class="fu">starts_with</span>(<span class="st">&quot;T_&quot;</span>))</span>
<span id="cb78-1018"><a href="#cb78-1018" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1019"><a href="#cb78-1019" aria-hidden="true" tabindex="-1"></a><span class="fu">st</span>(covariates)</span>
<span id="cb78-1020"><a href="#cb78-1020" aria-hidden="true" tabindex="-1"></a><span class="fu">st</span>(covariates, <span class="at">out =</span> <span class="st">&#39;latex&#39;</span>, <span class="at">file =</span> <span class="st">&quot;2_output/tab_A2.tex&quot;</span>)</span>
<span id="cb78-1021"><a href="#cb78-1021" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1022"><a href="#cb78-1022" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1023"><a href="#cb78-1023" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1024"><a href="#cb78-1024" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1025"><a href="#cb78-1025" aria-hidden="true" tabindex="-1"></a><span class="fu">## Table A4: Main Results</span></span>
<span id="cb78-1026"><a href="#cb78-1026" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , results=&quot;asis&quot;, message=FALSE, echo=FALSE,warning=FALSE}</span></span>
<span id="cb78-1027"><a href="#cb78-1027" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1028"><a href="#cb78-1028" aria-hidden="true" tabindex="-1"></a>main<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>Ask<span class="sc">+</span>EQ<span class="sc">+</span>Honesty,</span>
<span id="cb78-1029"><a href="#cb78-1029" aria-hidden="true" tabindex="-1"></a>                  <span class="at">data=</span>df_long,<span class="at">clusters =</span> CaseID, <span class="at">se_type =</span> <span class="st">&quot;stata&quot;</span>)</span>
<span id="cb78-1030"><a href="#cb78-1030" aria-hidden="true" tabindex="-1"></a><span class="co"># Update term names in the model object</span></span>
<span id="cb78-1031"><a href="#cb78-1031" aria-hidden="true" tabindex="-1"></a>main<span class="sc">$</span>term <span class="ot">&lt;-</span> <span class="fu">recode</span>(</span>
<span id="cb78-1032"><a href="#cb78-1032" aria-hidden="true" tabindex="-1"></a>  main<span class="sc">$</span>term,</span>
<span id="cb78-1033"><a href="#cb78-1033" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">(Intercept)</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Intercept&quot;</span>,</span>
<span id="cb78-1034"><a href="#cb78-1034" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort&quot;</span>,</span>
<span id="cb78-1035"><a href="#cb78-1035" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_reliefRelief Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort&quot;</span>,</span>
<span id="cb78-1036"><a href="#cb78-1036" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_preventionPrevention Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effective&quot;</span>,</span>
<span id="cb78-1037"><a href="#cb78-1037" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_reliefRelief Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effective&quot;</span>,</span>
<span id="cb78-1038"><a href="#cb78-1038" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">AskAsk Relief</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Ask&quot;</span>,</span>
<span id="cb78-1039"><a href="#cb78-1039" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">EQVisits</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb78-1040"><a href="#cb78-1040" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">HonestyCorruption</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span>,</span>
<span id="cb78-1041"><a href="#cb78-1041" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">HonestyVote Buying</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying&quot;</span></span>
<span id="cb78-1042"><a href="#cb78-1042" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1043"><a href="#cb78-1043" aria-hidden="true" tabindex="-1"></a><span class="fu">msummary</span>(main, </span>
<span id="cb78-1044"><a href="#cb78-1044" aria-hidden="true" tabindex="-1"></a>         <span class="at">title =</span> <span class="st">&#39;Main Results&#39;</span>, </span>
<span id="cb78-1045"><a href="#cb78-1045" aria-hidden="true" tabindex="-1"></a>         <span class="at">output =</span> <span class="st">&quot;2_output/tab_A4_main_results.tex&quot;</span>, </span>
<span id="cb78-1046"><a href="#cb78-1046" aria-hidden="true" tabindex="-1"></a>         <span class="at">stars =</span> <span class="cn">TRUE</span>, </span>
<span id="cb78-1047"><a href="#cb78-1047" aria-hidden="true" tabindex="-1"></a>         <span class="at">fmt =</span> <span class="dv">2</span>)</span>
<span id="cb78-1048"><a href="#cb78-1048" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1049"><a href="#cb78-1049" aria-hidden="true" tabindex="-1"></a><span class="fu">msummary</span>(main, <span class="at">title =</span> <span class="st">&#39;Main Results&#39;</span>, <span class="at">output =</span> <span class="st">&quot;html&quot;</span>, <span class="at">stars =</span> <span class="cn">TRUE</span>, <span class="at">fmt =</span> <span class="dv">2</span>)</span>
<span id="cb78-1050"><a href="#cb78-1050" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1051"><a href="#cb78-1051" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1052"><a href="#cb78-1052" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1053"><a href="#cb78-1053" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A9: Marginal Means</span></span>
<span id="cb78-1054"><a href="#cb78-1054" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , results=&quot;asis&quot;, message=FALSE, echo=FALSE}</span></span>
<span id="cb78-1055"><a href="#cb78-1055" aria-hidden="true" tabindex="-1"></a>means<span class="ot">&lt;-</span><span class="fu">mm</span>(df_long, Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>Ask<span class="sc">+</span>EQ<span class="sc">+</span>Honesty ,<span class="at">id =</span> <span class="sc">~</span> CaseID, <span class="at">h0 =</span> <span class="fl">0.5</span>)</span>
<span id="cb78-1056"><a href="#cb78-1056" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1057"><a href="#cb78-1057" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1058"><a href="#cb78-1058" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;No Prevention Effort&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Effort&quot;</span></span>
<span id="cb78-1059"><a href="#cb78-1059" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Prevention Effort&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Effort&quot;</span></span>
<span id="cb78-1060"><a href="#cb78-1060" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;No Relief Effort&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Effort&quot;</span></span>
<span id="cb78-1061"><a href="#cb78-1061" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Relief Effort&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Effort&quot;</span></span>
<span id="cb78-1062"><a href="#cb78-1062" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1063"><a href="#cb78-1063" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Prevention Not Effective&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Effevtive&quot;</span></span>
<span id="cb78-1064"><a href="#cb78-1064" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Prevention Effective&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Effevtive&quot;</span></span>
<span id="cb78-1065"><a href="#cb78-1065" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;No Relief&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Effevtive&quot;</span></span>
<span id="cb78-1066"><a href="#cb78-1066" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Relief Effective&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Effevtive&quot;</span></span>
<span id="cb78-1067"><a href="#cb78-1067" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1068"><a href="#cb78-1068" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Not Ask Relief&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1069"><a href="#cb78-1069" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Ask Relief&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1070"><a href="#cb78-1070" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;No Visits&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1071"><a href="#cb78-1071" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Visits&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1072"><a href="#cb78-1072" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1073"><a href="#cb78-1073" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;No Corruption&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1074"><a href="#cb78-1074" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Corruption&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1075"><a href="#cb78-1075" aria-hidden="true" tabindex="-1"></a>means<span class="sc">$</span>group[means<span class="sc">$</span>level<span class="sc">==</span><span class="st">&quot;Vote Buying&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1076"><a href="#cb78-1076" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1077"><a href="#cb78-1077" aria-hidden="true" tabindex="-1"></a>fig_means<span class="ot">&lt;-</span>means<span class="sc">%&gt;%</span> </span>
<span id="cb78-1078"><a href="#cb78-1078" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_reorder</span>(level, <span class="fu">desc</span>(<span class="sc">-</span>estimate))) <span class="sc">%&gt;%</span></span>
<span id="cb78-1079"><a href="#cb78-1079" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> level, <span class="at">color =</span> <span class="fu">as.factor</span>(group))) <span class="sc">+</span></span>
<span id="cb78-1080"><a href="#cb78-1080" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-1081"><a href="#cb78-1081" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> level, </span>
<span id="cb78-1082"><a href="#cb78-1082" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> estimate <span class="sc">-</span> <span class="fl">1.96</span><span class="sc">*</span>std.error, </span>
<span id="cb78-1083"><a href="#cb78-1083" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> estimate <span class="sc">+</span> <span class="fl">1.96</span><span class="sc">*</span>std.error),</span>
<span id="cb78-1084"><a href="#cb78-1084" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-1085"><a href="#cb78-1085" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_colour_viridis_d</span>(<span class="at">name =</span> <span class="st">&#39;group&#39;</span>) <span class="sc">+</span> </span>
<span id="cb78-1086"><a href="#cb78-1086" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-1087"><a href="#cb78-1087" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Marginal Means&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1088"><a href="#cb78-1088" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1089"><a href="#cb78-1089" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y=</span><span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb78-1090"><a href="#cb78-1090" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1091"><a href="#cb78-1091" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-1092"><a href="#cb78-1092" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="fl">0.5</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-1093"><a href="#cb78-1093" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)</span>
<span id="cb78-1094"><a href="#cb78-1094" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1095"><a href="#cb78-1095" aria-hidden="true" tabindex="-1"></a>fig_means</span>
<span id="cb78-1096"><a href="#cb78-1096" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1097"><a href="#cb78-1097" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1098"><a href="#cb78-1098" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A9_mm.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1099"><a href="#cb78-1099" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1100"><a href="#cb78-1100" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1101"><a href="#cb78-1101" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1102"><a href="#cb78-1102" aria-hidden="true" tabindex="-1"></a><span class="fu">## Table A5: Main Results, Linear Hypothesis 1</span></span>
<span id="cb78-1103"><a href="#cb78-1103" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1104"><a href="#cb78-1104" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , results=&quot;asis&quot;, message=FALSE, echo=FALSE}</span></span>
<span id="cb78-1105"><a href="#cb78-1105" aria-hidden="true" tabindex="-1"></a>lhro1 <span class="ot">&lt;-</span> <span class="fu">lh_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>Ask<span class="sc">+</span>EQ<span class="sc">+</span>Honesty, <span class="at">data =</span> df_long, <span class="at">linear_hypothesis =</span> <span class="st">&quot;Effort_preventionPrevention Effort = Effort_reliefRelief Effort&quot;</span>)</span>
<span id="cb78-1106"><a href="#cb78-1106" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1107"><a href="#cb78-1107" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename terms in the lm_robust part</span></span>
<span id="cb78-1108"><a href="#cb78-1108" aria-hidden="true" tabindex="-1"></a>lhro1<span class="sc">$</span>lm_robust<span class="sc">$</span>term <span class="ot">&lt;-</span> <span class="fu">recode</span>(</span>
<span id="cb78-1109"><a href="#cb78-1109" aria-hidden="true" tabindex="-1"></a>  lhro1<span class="sc">$</span>lm_robust<span class="sc">$</span>term,</span>
<span id="cb78-1110"><a href="#cb78-1110" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">(Intercept)</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Intercept&quot;</span>,</span>
<span id="cb78-1111"><a href="#cb78-1111" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort&quot;</span>,</span>
<span id="cb78-1112"><a href="#cb78-1112" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_reliefRelief Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort&quot;</span>,</span>
<span id="cb78-1113"><a href="#cb78-1113" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_preventionPrevention Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effective&quot;</span>,</span>
<span id="cb78-1114"><a href="#cb78-1114" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_reliefRelief Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effective&quot;</span>,</span>
<span id="cb78-1115"><a href="#cb78-1115" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">AskAsk Relief</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Ask&quot;</span>,</span>
<span id="cb78-1116"><a href="#cb78-1116" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">EQVisits</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb78-1117"><a href="#cb78-1117" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">HonestyCorruption</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span>,</span>
<span id="cb78-1118"><a href="#cb78-1118" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">HonestyVote Buying</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying&quot;</span></span>
<span id="cb78-1119"><a href="#cb78-1119" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1120"><a href="#cb78-1120" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1121"><a href="#cb78-1121" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename terms in the linear hypothesis (lh) part</span></span>
<span id="cb78-1122"><a href="#cb78-1122" aria-hidden="true" tabindex="-1"></a>lhro1<span class="sc">$</span>lh<span class="sc">$</span>term <span class="ot">&lt;-</span> <span class="fu">recode</span>(</span>
<span id="cb78-1123"><a href="#cb78-1123" aria-hidden="true" tabindex="-1"></a>  lhro1<span class="sc">$</span>lh<span class="sc">$</span>term,</span>
<span id="cb78-1124"><a href="#cb78-1124" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort = Effort_reliefRelief Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort = Relief Effort&quot;</span></span>
<span id="cb78-1125"><a href="#cb78-1125" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1126"><a href="#cb78-1126" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1127"><a href="#cb78-1127" aria-hidden="true" tabindex="-1"></a><span class="fu">msummary</span>(lhro1, </span>
<span id="cb78-1128"><a href="#cb78-1128" aria-hidden="true" tabindex="-1"></a>         <span class="at">title =</span> <span class="st">&#39;Main Results, Linear Hypothesis 1&#39;</span>, </span>
<span id="cb78-1129"><a href="#cb78-1129" aria-hidden="true" tabindex="-1"></a>         <span class="at">output =</span> <span class="st">&quot;2_output/tab_A5_linear_hypothesis_1.tex&quot;</span>, </span>
<span id="cb78-1130"><a href="#cb78-1130" aria-hidden="true" tabindex="-1"></a>         <span class="at">stars =</span> <span class="cn">TRUE</span>, </span>
<span id="cb78-1131"><a href="#cb78-1131" aria-hidden="true" tabindex="-1"></a>         <span class="at">fmt =</span> <span class="dv">2</span>)</span>
<span id="cb78-1132"><a href="#cb78-1132" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1133"><a href="#cb78-1133" aria-hidden="true" tabindex="-1"></a><span class="fu">msummary</span>(lhro1, <span class="at">title =</span> <span class="st">&#39;Main Results, Linear hypothesis 1&#39;</span>,<span class="at">output =</span> <span class="st">&quot;html&quot;</span>,<span class="at">stars =</span> <span class="cn">TRUE</span>,<span class="at">fmt =</span> <span class="dv">2</span>)</span>
<span id="cb78-1134"><a href="#cb78-1134" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1135"><a href="#cb78-1135" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1136"><a href="#cb78-1136" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1137"><a href="#cb78-1137" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1138"><a href="#cb78-1138" aria-hidden="true" tabindex="-1"></a><span class="fu">## Table A6: Main Results, Linear Hypothesis 2</span></span>
<span id="cb78-1139"><a href="#cb78-1139" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , results=&quot;asis&quot;, message=FALSE, echo=FALSE,warning=FALSE}</span></span>
<span id="cb78-1140"><a href="#cb78-1140" aria-hidden="true" tabindex="-1"></a>lhro2 <span class="ot">&lt;-</span> <span class="fu">lh_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>Ask<span class="sc">+</span>EQ<span class="sc">+</span>Honesty, <span class="at">data =</span> df_long, <span class="at">linear_hypothesis =</span> <span class="st">&quot;Effective_preventionPrevention Effective  = Effective_reliefRelief Effective &quot;</span>)</span>
<span id="cb78-1141"><a href="#cb78-1141" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1142"><a href="#cb78-1142" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1143"><a href="#cb78-1143" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename terms in the lm_robust part</span></span>
<span id="cb78-1144"><a href="#cb78-1144" aria-hidden="true" tabindex="-1"></a>lhro2<span class="sc">$</span>lm_robust<span class="sc">$</span>term <span class="ot">&lt;-</span> <span class="fu">recode</span>(</span>
<span id="cb78-1145"><a href="#cb78-1145" aria-hidden="true" tabindex="-1"></a>  lhro2<span class="sc">$</span>lm_robust<span class="sc">$</span>term,</span>
<span id="cb78-1146"><a href="#cb78-1146" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">(Intercept)</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Intercept&quot;</span>,</span>
<span id="cb78-1147"><a href="#cb78-1147" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort&quot;</span>,</span>
<span id="cb78-1148"><a href="#cb78-1148" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_reliefRelief Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort&quot;</span>,</span>
<span id="cb78-1149"><a href="#cb78-1149" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_preventionPrevention Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effective&quot;</span>,</span>
<span id="cb78-1150"><a href="#cb78-1150" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_reliefRelief Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effective&quot;</span>,</span>
<span id="cb78-1151"><a href="#cb78-1151" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">AskAsk Relief</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Ask&quot;</span>,</span>
<span id="cb78-1152"><a href="#cb78-1152" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">EQVisits</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb78-1153"><a href="#cb78-1153" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">HonestyCorruption</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span>,</span>
<span id="cb78-1154"><a href="#cb78-1154" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">HonestyVote Buying</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying&quot;</span></span>
<span id="cb78-1155"><a href="#cb78-1155" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1156"><a href="#cb78-1156" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1157"><a href="#cb78-1157" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename terms in the linear hypothesis (lh) part</span></span>
<span id="cb78-1158"><a href="#cb78-1158" aria-hidden="true" tabindex="-1"></a>lhro2<span class="sc">$</span>lh<span class="sc">$</span>term <span class="ot">&lt;-</span> <span class="fu">recode</span>(</span>
<span id="cb78-1159"><a href="#cb78-1159" aria-hidden="true" tabindex="-1"></a>  lhro2<span class="sc">$</span>lh<span class="sc">$</span>term,</span>
<span id="cb78-1160"><a href="#cb78-1160" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_preventionPrevention Effective  = Effective_reliefRelief Effective </span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effective = Relief Effective&quot;</span></span>
<span id="cb78-1161"><a href="#cb78-1161" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1162"><a href="#cb78-1162" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1163"><a href="#cb78-1163" aria-hidden="true" tabindex="-1"></a><span class="fu">msummary</span>(lhro2, </span>
<span id="cb78-1164"><a href="#cb78-1164" aria-hidden="true" tabindex="-1"></a>         <span class="at">title =</span> <span class="st">&#39;Main Results, Linear Hypothesis 2&#39;</span>, </span>
<span id="cb78-1165"><a href="#cb78-1165" aria-hidden="true" tabindex="-1"></a>         <span class="at">output =</span> <span class="st">&quot;2_output/tab_A6_linear_hypothesis_2.tex&quot;</span>, </span>
<span id="cb78-1166"><a href="#cb78-1166" aria-hidden="true" tabindex="-1"></a>         <span class="at">stars =</span> <span class="cn">TRUE</span>, </span>
<span id="cb78-1167"><a href="#cb78-1167" aria-hidden="true" tabindex="-1"></a>         <span class="at">fmt =</span> <span class="dv">2</span>)</span>
<span id="cb78-1168"><a href="#cb78-1168" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1169"><a href="#cb78-1169" aria-hidden="true" tabindex="-1"></a><span class="fu">msummary</span>(lhro2, <span class="at">title =</span> <span class="st">&#39;Main Results, Linear hypothesis 2&#39;</span>,<span class="at">output =</span> <span class="st">&quot;html&quot;</span>,<span class="at">stars =</span> <span class="cn">TRUE</span>,<span class="at">fmt =</span> <span class="dv">2</span>)</span>
<span id="cb78-1170"><a href="#cb78-1170" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1171"><a href="#cb78-1171" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1172"><a href="#cb78-1172" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1173"><a href="#cb78-1173" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1174"><a href="#cb78-1174" aria-hidden="true" tabindex="-1"></a><span class="fu">## Table A7: Main Results with Interactions</span></span>
<span id="cb78-1175"><a href="#cb78-1175" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1176"><a href="#cb78-1176" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , results=&quot;asis&quot;, message=FALSE, echo=FALSE,warning=FALSE}</span></span>
<span id="cb78-1177"><a href="#cb78-1177" aria-hidden="true" tabindex="-1"></a>interaction_model <span class="ot">&lt;-</span> <span class="fu">lm_robust</span>(</span>
<span id="cb78-1178"><a href="#cb78-1178" aria-hidden="true" tabindex="-1"></a>  Choice <span class="sc">~</span> Effort_prevention<span class="sc">+</span>Effort_relief <span class="sc">+</span></span>
<span id="cb78-1179"><a href="#cb78-1179" aria-hidden="true" tabindex="-1"></a>    Effective_prevention <span class="sc">+</span> Effective_relief <span class="sc">+</span></span>
<span id="cb78-1180"><a href="#cb78-1180" aria-hidden="true" tabindex="-1"></a>    Honesty<span class="sc">+</span></span>
<span id="cb78-1181"><a href="#cb78-1181" aria-hidden="true" tabindex="-1"></a>    Effective_prevention<span class="sc">*</span> Honesty<span class="sc">+</span></span>
<span id="cb78-1182"><a href="#cb78-1182" aria-hidden="true" tabindex="-1"></a>    Effort_relief<span class="sc">*</span>Effective_relief<span class="sc">+</span></span>
<span id="cb78-1183"><a href="#cb78-1183" aria-hidden="true" tabindex="-1"></a>    Effective_prevention<span class="sc">*</span> Effective_relief<span class="sc">+</span></span>
<span id="cb78-1184"><a href="#cb78-1184" aria-hidden="true" tabindex="-1"></a>    Effort_prevention <span class="sc">*</span> Effort_relief<span class="sc">+</span></span>
<span id="cb78-1185"><a href="#cb78-1185" aria-hidden="true" tabindex="-1"></a>    Effort_prevention <span class="sc">*</span> Effective_prevention<span class="sc">+</span></span>
<span id="cb78-1186"><a href="#cb78-1186" aria-hidden="true" tabindex="-1"></a>    Effort_prevention <span class="sc">*</span> Honesty <span class="sc">+</span></span>
<span id="cb78-1187"><a href="#cb78-1187" aria-hidden="true" tabindex="-1"></a>    Effective_relief<span class="sc">*</span>Honesty,</span>
<span id="cb78-1188"><a href="#cb78-1188" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> df_long,</span>
<span id="cb78-1189"><a href="#cb78-1189" aria-hidden="true" tabindex="-1"></a>  <span class="at">clusters =</span> CaseID,</span>
<span id="cb78-1190"><a href="#cb78-1190" aria-hidden="true" tabindex="-1"></a>  <span class="at">se_type =</span> <span class="st">&quot;stata&quot;</span></span>
<span id="cb78-1191"><a href="#cb78-1191" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1192"><a href="#cb78-1192" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1193"><a href="#cb78-1193" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename terms in the interaction model</span></span>
<span id="cb78-1194"><a href="#cb78-1194" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename terms in the interaction model</span></span>
<span id="cb78-1195"><a href="#cb78-1195" aria-hidden="true" tabindex="-1"></a>interaction_model<span class="sc">$</span>term <span class="ot">&lt;-</span> <span class="fu">recode</span>(</span>
<span id="cb78-1196"><a href="#cb78-1196" aria-hidden="true" tabindex="-1"></a>  interaction_model<span class="sc">$</span>term,</span>
<span id="cb78-1197"><a href="#cb78-1197" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">(Intercept)</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Intercept&quot;</span>,</span>
<span id="cb78-1198"><a href="#cb78-1198" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort&quot;</span>,</span>
<span id="cb78-1199"><a href="#cb78-1199" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_reliefRelief Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort&quot;</span>,</span>
<span id="cb78-1200"><a href="#cb78-1200" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_preventionPrevention Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effective&quot;</span>,</span>
<span id="cb78-1201"><a href="#cb78-1201" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_reliefRelief Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effective&quot;</span>,</span>
<span id="cb78-1202"><a href="#cb78-1202" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">HonestyCorruption</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span>,</span>
<span id="cb78-1203"><a href="#cb78-1203" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">HonestyVote Buying</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying&quot;</span>,</span>
<span id="cb78-1204"><a href="#cb78-1204" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_reliefRelief Effort:Effective_reliefRelief Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort × Relief Effective&quot;</span>,</span>
<span id="cb78-1205"><a href="#cb78-1205" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_preventionPrevention Effective:Effective_reliefRelief Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effective × Relief Effective&quot;</span>,</span>
<span id="cb78-1206"><a href="#cb78-1206" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort:Effort_reliefRelief Effort</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort × Relief Effort&quot;</span>,</span>
<span id="cb78-1207"><a href="#cb78-1207" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort:Effective_preventionPrevention Effective</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort × Preparedness Effective&quot;</span>,</span>
<span id="cb78-1208"><a href="#cb78-1208" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort:HonestyCorruption</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort × Corruption&quot;</span>,</span>
<span id="cb78-1209"><a href="#cb78-1209" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effort_preventionPrevention Effort:HonestyVote Buying</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effort × Vote Buying&quot;</span>,</span>
<span id="cb78-1210"><a href="#cb78-1210" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_preventionPrevention Effective:HonestyCorruption</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effective × Corruption&quot;</span>,</span>
<span id="cb78-1211"><a href="#cb78-1211" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_preventionPrevention Effective:HonestyVote Buying</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effective × Vote Buying&quot;</span>,</span>
<span id="cb78-1212"><a href="#cb78-1212" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_reliefRelief Effective:HonestyCorruption</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effective × Corruption&quot;</span>,</span>
<span id="cb78-1213"><a href="#cb78-1213" aria-hidden="true" tabindex="-1"></a>  <span class="st">`</span><span class="at">Effective_reliefRelief Effective:HonestyVote Buying</span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;Relief Effective × Vote Buying&quot;</span></span>
<span id="cb78-1214"><a href="#cb78-1214" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1215"><a href="#cb78-1215" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1216"><a href="#cb78-1216" aria-hidden="true" tabindex="-1"></a><span class="co"># Display the table in QMD and save as LaTeX</span></span>
<span id="cb78-1217"><a href="#cb78-1217" aria-hidden="true" tabindex="-1"></a><span class="fu">msummary</span>(</span>
<span id="cb78-1218"><a href="#cb78-1218" aria-hidden="true" tabindex="-1"></a>  interaction_model,</span>
<span id="cb78-1219"><a href="#cb78-1219" aria-hidden="true" tabindex="-1"></a>  <span class="at">title =</span> <span class="st">&#39;Table A7: Main Results with Interactions&#39;</span>, </span>
<span id="cb78-1220"><a href="#cb78-1220" aria-hidden="true" tabindex="-1"></a>  <span class="at">output =</span> <span class="st">&quot;2_output/tab_A7_interactions.tex&quot;</span>, </span>
<span id="cb78-1221"><a href="#cb78-1221" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="cn">TRUE</span>,</span>
<span id="cb78-1222"><a href="#cb78-1222" aria-hidden="true" tabindex="-1"></a>  <span class="at">fmt =</span> <span class="dv">2</span></span>
<span id="cb78-1223"><a href="#cb78-1223" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1224"><a href="#cb78-1224" aria-hidden="true" tabindex="-1"></a><span class="fu">msummary</span>(interaction_model, <span class="at">title =</span> <span class="st">&#39;Table A7: Main Results with Interactions&#39;</span>,<span class="at">output =</span> <span class="st">&quot;html&quot;</span>,<span class="at">stars =</span> <span class="cn">TRUE</span>,<span class="at">fmt =</span> <span class="dv">2</span>)</span>
<span id="cb78-1225"><a href="#cb78-1225" aria-hidden="true" tabindex="-1"></a>         </span>
<span id="cb78-1226"><a href="#cb78-1226" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1227"><a href="#cb78-1227" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1228"><a href="#cb78-1228" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1229"><a href="#cb78-1229" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1230"><a href="#cb78-1230" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A10: Heterogeneity of AMCE by Number of Contest</span></span>
<span id="cb78-1231"><a href="#cb78-1231" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , results=&quot;asis&quot;, message=FALSE, echo=FALSE,warning=FALSE}</span></span>
<span id="cb78-1232"><a href="#cb78-1232" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1233"><a href="#cb78-1233" aria-hidden="true" tabindex="-1"></a>  df_long_main<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span> dplyr<span class="sc">::</span><span class="fu">rename</span>(</span>
<span id="cb78-1234"><a href="#cb78-1234" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffortPrevention_ =</span> Effort_prevention,</span>
<span id="cb78-1235"><a href="#cb78-1235" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffortRelief_ =</span> Effort_relief,</span>
<span id="cb78-1236"><a href="#cb78-1236" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffectivePrevention_ =</span> Effective_prevention,</span>
<span id="cb78-1237"><a href="#cb78-1237" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffectiveRelief_ =</span> Effective_relief,</span>
<span id="cb78-1238"><a href="#cb78-1238" aria-hidden="true" tabindex="-1"></a>                          <span class="at">Ask_ =</span> Ask,</span>
<span id="cb78-1239"><a href="#cb78-1239" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EQ_ =</span> EQ,</span>
<span id="cb78-1240"><a href="#cb78-1240" aria-hidden="true" tabindex="-1"></a>                          <span class="at">Honesty_ =</span> Honesty</span>
<span id="cb78-1241"><a href="#cb78-1241" aria-hidden="true" tabindex="-1"></a>                          )</span>
<span id="cb78-1242"><a href="#cb78-1242" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create an empty data frame to store the results</span></span>
<span id="cb78-1243"><a href="#cb78-1243" aria-hidden="true" tabindex="-1"></a>  final_results <span class="ot">&lt;-</span> <span class="fu">data.frame</span>()</span>
<span id="cb78-1244"><a href="#cb78-1244" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1245"><a href="#cb78-1245" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Get unique contests</span></span>
<span id="cb78-1246"><a href="#cb78-1246" aria-hidden="true" tabindex="-1"></a>  unique_contests <span class="ot">&lt;-</span> <span class="fu">unique</span>(df_long_main<span class="sc">$</span>contest)</span>
<span id="cb78-1247"><a href="#cb78-1247" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1248"><a href="#cb78-1248" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Loop through each unique contest</span></span>
<span id="cb78-1249"><a href="#cb78-1249" aria-hidden="true" tabindex="-1"></a>  <span class="cf">for</span> (contest_value <span class="cf">in</span> unique_contests) {</span>
<span id="cb78-1250"><a href="#cb78-1250" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create a subset of the dataframe for the current contest value</span></span>
<span id="cb78-1251"><a href="#cb78-1251" aria-hidden="true" tabindex="-1"></a>  contest_data <span class="ot">&lt;-</span> df_long_main <span class="sc">%&gt;%</span> <span class="fu">filter</span>(contest <span class="sc">==</span> contest_value)</span>
<span id="cb78-1252"><a href="#cb78-1252" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1253"><a href="#cb78-1253" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Perform the analysis for the current contest</span></span>
<span id="cb78-1254"><a href="#cb78-1254" aria-hidden="true" tabindex="-1"></a>  res <span class="ot">&lt;-</span> <span class="fu">lm_robust</span>(Choice <span class="sc">~</span>EffortPrevention_<span class="sc">+</span>EffortRelief_<span class="sc">+</span>EffectiveRelief_<span class="sc">+</span>EffectivePrevention_<span class="sc">+</span>Ask_<span class="sc">+</span>EQ_<span class="sc">+</span>Honesty_,</span>
<span id="cb78-1255"><a href="#cb78-1255" aria-hidden="true" tabindex="-1"></a>                   <span class="at">data =</span> contest_data, <span class="at">clusters =</span> CaseID, <span class="at">se_type =</span> <span class="st">&quot;stata&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-1256"><a href="#cb78-1256" aria-hidden="true" tabindex="-1"></a>    <span class="fu">tidy_coefs_with_ref</span>() <span class="sc">%&gt;%</span></span>
<span id="cb78-1257"><a href="#cb78-1257" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="sc">!</span>(variable <span class="sc">==</span> <span class="st">&#39;(Intercept)_&#39;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-1258"><a href="#cb78-1258" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">paste</span>(level, <span class="st">&quot;   &quot;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-1259"><a href="#cb78-1259" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">estimate =</span> tidyr<span class="sc">::</span><span class="fu">replace_na</span>(estimate, <span class="dv">0</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-1260"><a href="#cb78-1260" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1261"><a href="#cb78-1261" aria-hidden="true" tabindex="-1"></a>      <span class="at">variable =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(variable, </span>
<span id="cb78-1262"><a href="#cb78-1262" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;Ask_&quot;</span><span class="ot">=</span><span class="st">&quot;E. Ask_&quot;</span>, </span>
<span id="cb78-1263"><a href="#cb78-1263" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;Honesty_&quot;</span> <span class="ot">=</span> <span class="st">&quot;G. Honesty_&quot;</span>,</span>
<span id="cb78-1264"><a href="#cb78-1264" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EQ_&quot;</span> <span class="ot">=</span> <span class="st">&quot;F. EQ_&quot;</span>,</span>
<span id="cb78-1265"><a href="#cb78-1265" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EffectivePrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;C. EffectivePrevention_&quot;</span>,</span>
<span id="cb78-1266"><a href="#cb78-1266" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EffectiveRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;D. EffectiveRelief_&quot;</span>,</span>
<span id="cb78-1267"><a href="#cb78-1267" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EffortPrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;A. EffortPrevention_&quot;</span>,</span>
<span id="cb78-1268"><a href="#cb78-1268" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EffortRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;B. EffortRelief_&quot;</span></span>
<span id="cb78-1269"><a href="#cb78-1269" aria-hidden="true" tabindex="-1"></a>      )) <span class="sc">%&gt;%</span></span>
<span id="cb78-1270"><a href="#cb78-1270" aria-hidden="true" tabindex="-1"></a>    <span class="co"># add empty raw</span></span>
<span id="cb78-1271"><a href="#cb78-1271" aria-hidden="true" tabindex="-1"></a>    <span class="fu">as_tibble</span>() <span class="sc">%&gt;%</span></span>
<span id="cb78-1272"><a href="#cb78-1272" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(variable) <span class="sc">%&gt;%</span> </span>
<span id="cb78-1273"><a href="#cb78-1273" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_modify</span>(<span class="sc">~</span> <span class="fu">add_row</span>(.x,<span class="at">.before=</span><span class="dv">0</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-1274"><a href="#cb78-1274" aria-hidden="true" tabindex="-1"></a>    <span class="co"># reorder</span></span>
<span id="cb78-1275"><a href="#cb78-1275" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_reorder</span>(level, estimate,<span class="at">.na_rm =</span> <span class="cn">TRUE</span>))  <span class="sc">%&gt;%</span> </span>
<span id="cb78-1276"><a href="#cb78-1276" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> dplyr<span class="sc">::</span><span class="fu">coalesce</span>(level,variable))   <span class="sc">%&gt;%</span> </span>
<span id="cb78-1277"><a href="#cb78-1277" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1278"><a href="#cb78-1278" aria-hidden="true" tabindex="-1"></a>      <span class="at">level =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(level, </span>
<span id="cb78-1279"><a href="#cb78-1279" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;A. EffortPrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effort Preparedness&quot;</span>,</span>
<span id="cb78-1280"><a href="#cb78-1280" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;B. EffortRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effort Relief&quot;</span>,</span>
<span id="cb78-1281"><a href="#cb78-1281" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;C. EffectivePrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effective Preparedness&quot;</span>,</span>
<span id="cb78-1282"><a href="#cb78-1282" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;D. EffectiveRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effective Relief&quot;</span>,</span>
<span id="cb78-1283"><a href="#cb78-1283" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;E. Ask_&quot;</span><span class="ot">=</span><span class="st">&quot;Ask Help&quot;</span>, </span>
<span id="cb78-1284"><a href="#cb78-1284" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;F. EQ_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb78-1285"><a href="#cb78-1285" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;G. Honesty_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span></span>
<span id="cb78-1286"><a href="#cb78-1286" aria-hidden="true" tabindex="-1"></a>      )) <span class="sc">%&gt;%</span></span>
<span id="cb78-1287"><a href="#cb78-1287" aria-hidden="true" tabindex="-1"></a>    <span class="fu">add_column</span>(<span class="at">indicator =</span> <span class="dv">0</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-1288"><a href="#cb78-1288" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1289"><a href="#cb78-1289" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;A. EffortPrevention_&quot;</span>, <span class="st">&quot;Effort&quot;</span>),</span>
<span id="cb78-1290"><a href="#cb78-1290" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;B. EffortRelief_&quot;</span>, <span class="st">&quot;Effort&quot;</span>),</span>
<span id="cb78-1291"><a href="#cb78-1291" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;C. EffectivePrevention_&quot;</span>, <span class="st">&quot;Effective&quot;</span>),</span>
<span id="cb78-1292"><a href="#cb78-1292" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;D. EffectiveRelief_&quot;</span>, <span class="st">&quot;Effective&quot;</span>),</span>
<span id="cb78-1293"><a href="#cb78-1293" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;E. Ask_&quot;</span>, <span class="st">&quot;Other&quot;</span>),</span>
<span id="cb78-1294"><a href="#cb78-1294" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;F. EQ_&quot;</span>, <span class="st">&quot;Other&quot;</span>),</span>
<span id="cb78-1295"><a href="#cb78-1295" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;G. Honesty_&quot;</span>, <span class="st">&quot;Other&quot;</span>)</span>
<span id="cb78-1296"><a href="#cb78-1296" aria-hidden="true" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb78-1297"><a href="#cb78-1297" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1298"><a href="#cb78-1298" aria-hidden="true" tabindex="-1"></a>      <span class="at">level =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(level, </span>
<span id="cb78-1299"><a href="#cb78-1299" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Prevention Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot;Yes    &quot;</span>, </span>
<span id="cb78-1300"><a href="#cb78-1300" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Prevention Not Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot;No    &quot;</span>,</span>
<span id="cb78-1301"><a href="#cb78-1301" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Relief Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot; Yes    &quot;</span>,</span>
<span id="cb78-1302"><a href="#cb78-1302" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;No Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot; No    &quot;</span>,</span>
<span id="cb78-1303"><a href="#cb78-1303" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Prevention Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot;High    &quot;</span>,</span>
<span id="cb78-1304"><a href="#cb78-1304" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;No Prevention Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot;Low    &quot;</span>,</span>
<span id="cb78-1305"><a href="#cb78-1305" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Relief Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot; High   &quot;</span>,</span>
<span id="cb78-1306"><a href="#cb78-1306" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;No Relief Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot; Low    &quot;</span>,</span>
<span id="cb78-1307"><a href="#cb78-1307" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Ask Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot;  Yes    &quot;</span>,</span>
<span id="cb78-1308"><a href="#cb78-1308" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Not Ask Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot;  No    &quot;</span>,</span>
<span id="cb78-1309"><a href="#cb78-1309" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;No Visits    &quot;</span> <span class="ot">=</span> <span class="st">&quot;   No    &quot;</span>,</span>
<span id="cb78-1310"><a href="#cb78-1310" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Visits    &quot;</span> <span class="ot">=</span> <span class="st">&quot;   Yes    &quot;</span></span>
<span id="cb78-1311"><a href="#cb78-1311" aria-hidden="true" tabindex="-1"></a>      )</span>
<span id="cb78-1312"><a href="#cb78-1312" aria-hidden="true" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb78-1313"><a href="#cb78-1313" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">factor</span>(level, <span class="at">levels =</span> level)) <span class="sc">%&gt;%</span>  </span>
<span id="cb78-1314"><a href="#cb78-1314" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_rev</span>(<span class="fu">as_factor</span>(level)))</span>
<span id="cb78-1315"><a href="#cb78-1315" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Add a column indicating the contest for each observation</span></span>
<span id="cb78-1316"><a href="#cb78-1316" aria-hidden="true" tabindex="-1"></a>    res<span class="sc">$</span>contest <span class="ot">&lt;-</span> <span class="fu">paste</span>(<span class="st">&quot;Contest&quot;</span>, contest_value)</span>
<span id="cb78-1317"><a href="#cb78-1317" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1318"><a href="#cb78-1318" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Bind the results for the current contest to the final_results data frame</span></span>
<span id="cb78-1319"><a href="#cb78-1319" aria-hidden="true" tabindex="-1"></a>  final_results <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(final_results, res)</span>
<span id="cb78-1320"><a href="#cb78-1320" aria-hidden="true" tabindex="-1"></a>  }</span>
<span id="cb78-1321"><a href="#cb78-1321" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1322"><a href="#cb78-1322" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1323"><a href="#cb78-1323" aria-hidden="true" tabindex="-1"></a>  bold.ticks <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Ask Help&quot;</span>,<span class="st">&quot;Corruption&quot;</span>,<span class="st">&quot;Visits&quot;</span>,<span class="st">&quot;Effective Preparedness&quot;</span>,<span class="st">&quot;Effective Relief&quot;</span>,<span class="st">&quot;Effort Preparedness&quot;</span>,<span class="st">&quot;Effort Relief&quot;</span>)</span>
<span id="cb78-1324"><a href="#cb78-1324" aria-hidden="true" tabindex="-1"></a>  bold.labels <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">levels</span>(final_results<span class="sc">$</span>level) <span class="sc">%in%</span> bold.ticks, <span class="at">yes =</span> <span class="st">&quot;bold&quot;</span>, <span class="at">no =</span> <span class="st">&quot;plain&quot;</span>)</span>
<span id="cb78-1325"><a href="#cb78-1325" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1326"><a href="#cb78-1326" aria-hidden="true" tabindex="-1"></a>  fig_contest<span class="ot">&lt;-</span>final_results <span class="sc">%&gt;%</span>  </span>
<span id="cb78-1327"><a href="#cb78-1327" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span>  level,<span class="at">color =</span> indicator)) <span class="sc">+</span></span>
<span id="cb78-1328"><a href="#cb78-1328" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-1329"><a href="#cb78-1329" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> level, </span>
<span id="cb78-1330"><a href="#cb78-1330" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> conf.low, </span>
<span id="cb78-1331"><a href="#cb78-1331" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> conf.high),</span>
<span id="cb78-1332"><a href="#cb78-1332" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="dv">1</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-1333"><a href="#cb78-1333" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_colour_colorblind</span>()<span class="sc">+</span></span>
<span id="cb78-1334"><a href="#cb78-1334" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="fu">vars</span>(<span class="fu">as.factor</span>(contest)))<span class="sc">+</span></span>
<span id="cb78-1335"><a href="#cb78-1335" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-1336"><a href="#cb78-1336" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;AMCEs&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1337"><a href="#cb78-1337" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Effect on Probability of Support for Candidate&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1338"><a href="#cb78-1338" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Disaster Policy Choice&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1339"><a href="#cb78-1339" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1340"><a href="#cb78-1340" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-1341"><a href="#cb78-1341" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">face=</span>bold.labels))<span class="sc">+</span></span>
<span id="cb78-1342"><a href="#cb78-1342" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>,<span class="at">vjust=</span><span class="sc">-</span><span class="fl">2.5</span>))<span class="sc">+</span></span>
<span id="cb78-1343"><a href="#cb78-1343" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-1344"><a href="#cb78-1344" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1345"><a href="#cb78-1345" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;right&quot;</span>)</span>
<span id="cb78-1346"><a href="#cb78-1346" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1347"><a href="#cb78-1347" aria-hidden="true" tabindex="-1"></a>  fig_contest</span>
<span id="cb78-1348"><a href="#cb78-1348" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1349"><a href="#cb78-1349" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A10_contest.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">14</span>, <span class="at">height =</span> <span class="dv">8</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1350"><a href="#cb78-1350" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1351"><a href="#cb78-1351" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1352"><a href="#cb78-1352" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1353"><a href="#cb78-1353" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1354"><a href="#cb78-1354" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A11: Distance to Flood (2015) in 2018.</span></span>
<span id="cb78-1355"><a href="#cb78-1355" aria-hidden="true" tabindex="-1"></a><span class="in">```{r flood_distance, message=FALSE, warning=FALSE, echo=FALSE,out.width=&quot;60%&quot;,fig.align=&quot;center&quot;,fig.cap=c(&quot;Distance of survey respindents to Flood (2015) in 2018 (in meters)&quot;)}</span></span>
<span id="cb78-1356"><a href="#cb78-1356" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1357"><a href="#cb78-1357" aria-hidden="true" tabindex="-1"></a>  df_long_main <span class="ot">&lt;-</span> <span class="fu">left_join</span>(df_long,covariates, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;CaseID&quot;</span>)) </span>
<span id="cb78-1358"><a href="#cb78-1358" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1359"><a href="#cb78-1359" aria-hidden="true" tabindex="-1"></a>  df_long_main<span class="ot">&lt;-</span>df_long_main <span class="sc">%&gt;%</span> dplyr<span class="sc">::</span><span class="fu">rename</span>(</span>
<span id="cb78-1360"><a href="#cb78-1360" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffortPrevention_ =</span> Effort_prevention,</span>
<span id="cb78-1361"><a href="#cb78-1361" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffortRelief_ =</span> Effort_relief,</span>
<span id="cb78-1362"><a href="#cb78-1362" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffectivePrevention_ =</span> Effective_prevention,</span>
<span id="cb78-1363"><a href="#cb78-1363" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EffectiveRelief_ =</span> Effective_relief,</span>
<span id="cb78-1364"><a href="#cb78-1364" aria-hidden="true" tabindex="-1"></a>                          <span class="at">Ask_ =</span> Ask,</span>
<span id="cb78-1365"><a href="#cb78-1365" aria-hidden="true" tabindex="-1"></a>                          <span class="at">EQ_ =</span> EQ,</span>
<span id="cb78-1366"><a href="#cb78-1366" aria-hidden="true" tabindex="-1"></a>                          <span class="at">Honesty_ =</span> Honesty</span>
<span id="cb78-1367"><a href="#cb78-1367" aria-hidden="true" tabindex="-1"></a>                          )</span>
<span id="cb78-1368"><a href="#cb78-1368" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1369"><a href="#cb78-1369" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1370"><a href="#cb78-1370" aria-hidden="true" tabindex="-1"></a>  df_distance <span class="ot">&lt;-</span> df_long_main  <span class="sc">%&gt;%</span> </span>
<span id="cb78-1371"><a href="#cb78-1371" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(life2015 <span class="sc">==</span> <span class="st">&quot;2&quot;</span>)<span class="sc">%&gt;%</span> </span>
<span id="cb78-1372"><a href="#cb78-1372" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">distance_cat =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-1373"><a href="#cb78-1373" aria-hidden="true" tabindex="-1"></a>                              distance_flood <span class="sc">&gt;=</span><span class="dv">0</span> <span class="sc">&amp;</span> distance_flood <span class="sc">&lt;=</span><span class="dv">1000</span> <span class="sc">~</span> <span class="st">&quot;A. 0-1000m&quot;</span>,</span>
<span id="cb78-1374"><a href="#cb78-1374" aria-hidden="true" tabindex="-1"></a>                              distance_flood <span class="sc">&gt;=</span><span class="dv">1000</span> <span class="sc">&amp;</span> distance_flood <span class="sc">&lt;=</span><span class="dv">2500</span> <span class="sc">~</span> <span class="st">&quot;B. 1000-2500m&quot;</span>,</span>
<span id="cb78-1375"><a href="#cb78-1375" aria-hidden="true" tabindex="-1"></a>                              distance_flood <span class="sc">&gt;=</span><span class="dv">2500</span> <span class="sc">&amp;</span> distance_flood <span class="sc">&lt;=</span><span class="dv">5000</span> <span class="sc">~</span> <span class="st">&quot;C. 2500-5000m&quot;</span>,</span>
<span id="cb78-1376"><a href="#cb78-1376" aria-hidden="true" tabindex="-1"></a>                              distance_flood <span class="sc">&gt;=</span><span class="dv">5000</span> <span class="sc">&amp;</span> distance_flood <span class="sc">&lt;=</span><span class="dv">10000</span> <span class="sc">~</span> <span class="st">&quot;D. 5000-10000m&quot;</span>,</span>
<span id="cb78-1377"><a href="#cb78-1377" aria-hidden="true" tabindex="-1"></a>                              distance_flood <span class="sc">&gt;=</span><span class="dv">10000</span> <span class="sc">&amp;</span> distance_flood <span class="sc">&lt;=</span><span class="dv">15000</span> <span class="sc">~</span> <span class="st">&quot;E. 10000-15000m&quot;</span>,</span>
<span id="cb78-1378"><a href="#cb78-1378" aria-hidden="true" tabindex="-1"></a>                              distance_flood <span class="sc">&gt;=</span><span class="dv">15000</span> <span class="sc">~</span> <span class="st">&quot;F. 15000-20000m&quot;</span>))</span>
<span id="cb78-1379"><a href="#cb78-1379" aria-hidden="true" tabindex="-1"></a>  <span class="co"># plot distance</span></span>
<span id="cb78-1380"><a href="#cb78-1380" aria-hidden="true" tabindex="-1"></a>  flood_distance2018<span class="ot">&lt;-</span><span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb78-1381"><a href="#cb78-1381" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(df_distance, <span class="at">mapping=</span><span class="fu">aes</span>(<span class="fu">as.numeric</span>(distance_flood)),<span class="at">alpha =</span> <span class="fl">0.7</span>)<span class="sc">+</span></span>
<span id="cb78-1382"><a href="#cb78-1382" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&#39;Density&#39;</span>,<span class="at">x=</span><span class="st">&#39;Distance to Flood (2015) in 2018 (meters)&#39;</span>)<span class="sc">+</span></span>
<span id="cb78-1383"><a href="#cb78-1383" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() </span>
<span id="cb78-1384"><a href="#cb78-1384" aria-hidden="true" tabindex="-1"></a>  flood_distance2018</span>
<span id="cb78-1385"><a href="#cb78-1385" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1386"><a href="#cb78-1386" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A11_flood_distance2018.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">3</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1387"><a href="#cb78-1387" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1388"><a href="#cb78-1388" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1389"><a href="#cb78-1389" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure 2: AMCE’s by by distance to 2015 flood</span></span>
<span id="cb78-1390"><a href="#cb78-1390" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1391"><a href="#cb78-1391" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , results=&quot;asis&quot;, message=FALSE, echo=FALSE}</span></span>
<span id="cb78-1392"><a href="#cb78-1392" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1393"><a href="#cb78-1393" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create an empty data frame to store the results</span></span>
<span id="cb78-1394"><a href="#cb78-1394" aria-hidden="true" tabindex="-1"></a>  final_results <span class="ot">&lt;-</span> <span class="fu">data.frame</span>()</span>
<span id="cb78-1395"><a href="#cb78-1395" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1396"><a href="#cb78-1396" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Get unique contests</span></span>
<span id="cb78-1397"><a href="#cb78-1397" aria-hidden="true" tabindex="-1"></a>  unique_distance <span class="ot">&lt;-</span> <span class="fu">unique</span>(df_distance<span class="sc">$</span>distance_cat)</span>
<span id="cb78-1398"><a href="#cb78-1398" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1399"><a href="#cb78-1399" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Loop through each unique contest</span></span>
<span id="cb78-1400"><a href="#cb78-1400" aria-hidden="true" tabindex="-1"></a>  <span class="cf">for</span> (distance_value <span class="cf">in</span> unique_distance) {</span>
<span id="cb78-1401"><a href="#cb78-1401" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create a subset of the dataframe for the current distance_cat value</span></span>
<span id="cb78-1402"><a href="#cb78-1402" aria-hidden="true" tabindex="-1"></a>  distance_data <span class="ot">&lt;-</span> df_distance <span class="sc">%&gt;%</span> <span class="fu">filter</span>(distance_cat <span class="sc">==</span> distance_value)</span>
<span id="cb78-1403"><a href="#cb78-1403" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1404"><a href="#cb78-1404" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Perform the analysis for the current distance_cat</span></span>
<span id="cb78-1405"><a href="#cb78-1405" aria-hidden="true" tabindex="-1"></a>  res <span class="ot">&lt;-</span> <span class="fu">lm_robust</span>(Choice <span class="sc">~</span>EffortPrevention_<span class="sc">+</span>EffortRelief_<span class="sc">+</span>EffectiveRelief_<span class="sc">+</span>EffectivePrevention_<span class="sc">+</span>Ask_<span class="sc">+</span>EQ_<span class="sc">+</span>Honesty_,</span>
<span id="cb78-1406"><a href="#cb78-1406" aria-hidden="true" tabindex="-1"></a>                   <span class="at">data =</span> distance_data, <span class="at">clusters =</span> CaseID, <span class="at">se_type =</span> <span class="st">&quot;stata&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-1407"><a href="#cb78-1407" aria-hidden="true" tabindex="-1"></a>    <span class="fu">tidy_coefs_with_ref</span>() <span class="sc">%&gt;%</span></span>
<span id="cb78-1408"><a href="#cb78-1408" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="sc">!</span>(variable <span class="sc">==</span> <span class="st">&#39;(Intercept)_&#39;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-1409"><a href="#cb78-1409" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">paste</span>(level, <span class="st">&quot;   &quot;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-1410"><a href="#cb78-1410" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">estimate =</span> tidyr<span class="sc">::</span><span class="fu">replace_na</span>(estimate, <span class="dv">0</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-1411"><a href="#cb78-1411" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1412"><a href="#cb78-1412" aria-hidden="true" tabindex="-1"></a>      <span class="at">variable =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(variable, </span>
<span id="cb78-1413"><a href="#cb78-1413" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;Ask_&quot;</span><span class="ot">=</span><span class="st">&quot;E. Ask_&quot;</span>, </span>
<span id="cb78-1414"><a href="#cb78-1414" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;Honesty_&quot;</span> <span class="ot">=</span> <span class="st">&quot;G. Honesty_&quot;</span>,</span>
<span id="cb78-1415"><a href="#cb78-1415" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EQ_&quot;</span> <span class="ot">=</span> <span class="st">&quot;F. EQ_&quot;</span>,</span>
<span id="cb78-1416"><a href="#cb78-1416" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EffectivePrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;C. EffectivePrevention_&quot;</span>,</span>
<span id="cb78-1417"><a href="#cb78-1417" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EffectiveRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;D. EffectiveRelief_&quot;</span>,</span>
<span id="cb78-1418"><a href="#cb78-1418" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EffortPrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;A. EffortPrevention_&quot;</span>,</span>
<span id="cb78-1419"><a href="#cb78-1419" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;EffortRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;B. EffortRelief_&quot;</span></span>
<span id="cb78-1420"><a href="#cb78-1420" aria-hidden="true" tabindex="-1"></a>      )) <span class="sc">%&gt;%</span></span>
<span id="cb78-1421"><a href="#cb78-1421" aria-hidden="true" tabindex="-1"></a>    <span class="co"># add empty raw</span></span>
<span id="cb78-1422"><a href="#cb78-1422" aria-hidden="true" tabindex="-1"></a>    <span class="fu">as_tibble</span>() <span class="sc">%&gt;%</span></span>
<span id="cb78-1423"><a href="#cb78-1423" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(variable) <span class="sc">%&gt;%</span> </span>
<span id="cb78-1424"><a href="#cb78-1424" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_modify</span>(<span class="sc">~</span> <span class="fu">add_row</span>(.x,<span class="at">.before=</span><span class="dv">0</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb78-1425"><a href="#cb78-1425" aria-hidden="true" tabindex="-1"></a>    <span class="co"># reorder</span></span>
<span id="cb78-1426"><a href="#cb78-1426" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_reorder</span>(level, estimate,<span class="at">.na_rm =</span> <span class="cn">TRUE</span>))  <span class="sc">%&gt;%</span> </span>
<span id="cb78-1427"><a href="#cb78-1427" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> dplyr<span class="sc">::</span><span class="fu">coalesce</span>(level,variable))   <span class="sc">%&gt;%</span> </span>
<span id="cb78-1428"><a href="#cb78-1428" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1429"><a href="#cb78-1429" aria-hidden="true" tabindex="-1"></a>      <span class="at">level =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(level, </span>
<span id="cb78-1430"><a href="#cb78-1430" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;A. EffortPrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effort Preparedness&quot;</span>,</span>
<span id="cb78-1431"><a href="#cb78-1431" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;B. EffortRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effort Relief&quot;</span>,</span>
<span id="cb78-1432"><a href="#cb78-1432" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;C. EffectivePrevention_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effective Preparedness&quot;</span>,</span>
<span id="cb78-1433"><a href="#cb78-1433" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;D. EffectiveRelief_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Effective Relief&quot;</span>,</span>
<span id="cb78-1434"><a href="#cb78-1434" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;E. Ask_&quot;</span><span class="ot">=</span><span class="st">&quot;Ask Help&quot;</span>, </span>
<span id="cb78-1435"><a href="#cb78-1435" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;F. EQ_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb78-1436"><a href="#cb78-1436" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;G. Honesty_&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span></span>
<span id="cb78-1437"><a href="#cb78-1437" aria-hidden="true" tabindex="-1"></a>      )) <span class="sc">%&gt;%</span></span>
<span id="cb78-1438"><a href="#cb78-1438" aria-hidden="true" tabindex="-1"></a>    <span class="fu">add_column</span>(<span class="at">indicator =</span> <span class="dv">0</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-1439"><a href="#cb78-1439" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1440"><a href="#cb78-1440" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;A. EffortPrevention_&quot;</span>, <span class="st">&quot;Effort&quot;</span>),</span>
<span id="cb78-1441"><a href="#cb78-1441" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;B. EffortRelief_&quot;</span>, <span class="st">&quot;Effort&quot;</span>),</span>
<span id="cb78-1442"><a href="#cb78-1442" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;C. EffectivePrevention_&quot;</span>, <span class="st">&quot;Effective&quot;</span>),</span>
<span id="cb78-1443"><a href="#cb78-1443" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;D. EffectiveRelief_&quot;</span>, <span class="st">&quot;Effective&quot;</span>),</span>
<span id="cb78-1444"><a href="#cb78-1444" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;E. Ask_&quot;</span>, <span class="st">&quot;Other&quot;</span>),</span>
<span id="cb78-1445"><a href="#cb78-1445" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;F. EQ_&quot;</span>, <span class="st">&quot;Other&quot;</span>),</span>
<span id="cb78-1446"><a href="#cb78-1446" aria-hidden="true" tabindex="-1"></a>      <span class="at">indicator =</span> <span class="fu">replace</span>(indicator, variable <span class="sc">==</span> <span class="st">&quot;G. Honesty_&quot;</span>, <span class="st">&quot;Other&quot;</span>)</span>
<span id="cb78-1447"><a href="#cb78-1447" aria-hidden="true" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb78-1448"><a href="#cb78-1448" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1449"><a href="#cb78-1449" aria-hidden="true" tabindex="-1"></a>      <span class="at">level =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(level, </span>
<span id="cb78-1450"><a href="#cb78-1450" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Prevention Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot;Yes    &quot;</span>, </span>
<span id="cb78-1451"><a href="#cb78-1451" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Prevention Not Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot;No    &quot;</span>,</span>
<span id="cb78-1452"><a href="#cb78-1452" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Relief Effective    &quot;</span> <span class="ot">=</span> <span class="st">&quot; Yes    &quot;</span>,</span>
<span id="cb78-1453"><a href="#cb78-1453" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;No Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot; No    &quot;</span>,</span>
<span id="cb78-1454"><a href="#cb78-1454" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Prevention Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot;High    &quot;</span>,</span>
<span id="cb78-1455"><a href="#cb78-1455" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;No Prevention Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot;Low    &quot;</span>,</span>
<span id="cb78-1456"><a href="#cb78-1456" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Relief Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot; High   &quot;</span>,</span>
<span id="cb78-1457"><a href="#cb78-1457" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;No Relief Effort    &quot;</span> <span class="ot">=</span> <span class="st">&quot; Low    &quot;</span>,</span>
<span id="cb78-1458"><a href="#cb78-1458" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Ask Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot;  Yes    &quot;</span>,</span>
<span id="cb78-1459"><a href="#cb78-1459" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Not Ask Relief    &quot;</span> <span class="ot">=</span> <span class="st">&quot;  No    &quot;</span>,</span>
<span id="cb78-1460"><a href="#cb78-1460" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;No Visits    &quot;</span> <span class="ot">=</span> <span class="st">&quot;   No    &quot;</span>,</span>
<span id="cb78-1461"><a href="#cb78-1461" aria-hidden="true" tabindex="-1"></a>                            <span class="st">&quot;Visits    &quot;</span> <span class="ot">=</span> <span class="st">&quot;   Yes    &quot;</span></span>
<span id="cb78-1462"><a href="#cb78-1462" aria-hidden="true" tabindex="-1"></a>      )</span>
<span id="cb78-1463"><a href="#cb78-1463" aria-hidden="true" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb78-1464"><a href="#cb78-1464" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">factor</span>(level, <span class="at">levels =</span> level)) <span class="sc">%&gt;%</span>  </span>
<span id="cb78-1465"><a href="#cb78-1465" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_rev</span>(<span class="fu">as_factor</span>(level)))</span>
<span id="cb78-1466"><a href="#cb78-1466" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Add a column indicating the contest for each observation</span></span>
<span id="cb78-1467"><a href="#cb78-1467" aria-hidden="true" tabindex="-1"></a>    res<span class="sc">$</span>distance_cat <span class="ot">&lt;-</span> <span class="fu">paste</span>(<span class="st">&quot;&quot;</span>, distance_value)</span>
<span id="cb78-1468"><a href="#cb78-1468" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1469"><a href="#cb78-1469" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Bind the results for the current contest to the final_results data frame</span></span>
<span id="cb78-1470"><a href="#cb78-1470" aria-hidden="true" tabindex="-1"></a>  final_results <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(final_results, res)</span>
<span id="cb78-1471"><a href="#cb78-1471" aria-hidden="true" tabindex="-1"></a>  }</span>
<span id="cb78-1472"><a href="#cb78-1472" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1473"><a href="#cb78-1473" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1474"><a href="#cb78-1474" aria-hidden="true" tabindex="-1"></a>  bold.ticks <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Ask Help&quot;</span>,<span class="st">&quot;Corruption&quot;</span>,<span class="st">&quot;Visits&quot;</span>,<span class="st">&quot;Effective Preparedness&quot;</span>,<span class="st">&quot;Effective Relief&quot;</span>,<span class="st">&quot;Effort Preparedness&quot;</span>,<span class="st">&quot;Effort Relief&quot;</span>)</span>
<span id="cb78-1475"><a href="#cb78-1475" aria-hidden="true" tabindex="-1"></a>  bold.labels <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">levels</span>(final_results<span class="sc">$</span>level) <span class="sc">%in%</span> bold.ticks, <span class="at">yes =</span> <span class="st">&quot;bold&quot;</span>, <span class="at">no =</span> <span class="st">&quot;plain&quot;</span>)</span>
<span id="cb78-1476"><a href="#cb78-1476" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1477"><a href="#cb78-1477" aria-hidden="true" tabindex="-1"></a>  fig_distance_cat<span class="ot">&lt;-</span>final_results <span class="sc">%&gt;%</span>  </span>
<span id="cb78-1478"><a href="#cb78-1478" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span>  level,<span class="at">color =</span> indicator)) <span class="sc">+</span></span>
<span id="cb78-1479"><a href="#cb78-1479" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-1480"><a href="#cb78-1480" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> level, </span>
<span id="cb78-1481"><a href="#cb78-1481" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span> conf.low, </span>
<span id="cb78-1482"><a href="#cb78-1482" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> conf.high),</span>
<span id="cb78-1483"><a href="#cb78-1483" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="dv">1</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-1484"><a href="#cb78-1484" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_colour_colorblind</span>()<span class="sc">+</span></span>
<span id="cb78-1485"><a href="#cb78-1485" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="fu">vars</span>(<span class="fu">as.factor</span>(distance_cat)))<span class="sc">+</span></span>
<span id="cb78-1486"><a href="#cb78-1486" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-1487"><a href="#cb78-1487" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;AMCEs&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1488"><a href="#cb78-1488" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Effect on Probability of Support for Candidate&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1489"><a href="#cb78-1489" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Disaster Policy Choice&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1490"><a href="#cb78-1490" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1491"><a href="#cb78-1491" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-1492"><a href="#cb78-1492" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">face=</span>bold.labels))<span class="sc">+</span></span>
<span id="cb78-1493"><a href="#cb78-1493" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>,<span class="at">vjust=</span><span class="sc">-</span><span class="fl">2.5</span>))<span class="sc">+</span></span>
<span id="cb78-1494"><a href="#cb78-1494" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-1495"><a href="#cb78-1495" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1496"><a href="#cb78-1496" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-1497"><a href="#cb78-1497" aria-hidden="true" tabindex="-1"></a>  fig_distance_cat</span>
<span id="cb78-1498"><a href="#cb78-1498" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1499"><a href="#cb78-1499" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A12_distance_cat.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">14</span>, <span class="at">height =</span> <span class="dv">8</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1500"><a href="#cb78-1500" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1501"><a href="#cb78-1501" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1502"><a href="#cb78-1502" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1503"><a href="#cb78-1503" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1504"><a href="#cb78-1504" aria-hidden="true" tabindex="-1"></a><span class="fu">### Figure A13: Scatterplot Economic Distress (X) and Distance to Flood (Y)</span></span>
<span id="cb78-1505"><a href="#cb78-1505" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1506"><a href="#cb78-1506" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1507"><a href="#cb78-1507" aria-hidden="true" tabindex="-1"></a><span class="in">```{r scattter, message=FALSE, warning=FALSE, echo=FALSE,out.width=&quot;50%&quot;,fig.align=&quot;center&quot;,fig.cap=c(&quot;Scatter plot, Distance to the 2015 flood in meters (X-Axis) and seft-reported economic hardship due to the flood (Y-axis)&quot;)}</span></span>
<span id="cb78-1508"><a href="#cb78-1508" aria-hidden="true" tabindex="-1"></a>fig_harm_distance<span class="ot">&lt;-</span><span class="fu">ggplot</span>(covariates, <span class="fu">aes</span>(<span class="at">x=</span>flood_econ, <span class="at">y=</span>distance_flood)) <span class="sc">+</span> </span>
<span id="cb78-1509"><a href="#cb78-1509" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">shape=</span><span class="dv">18</span>, <span class="at">color=</span><span class="st">&quot;blue&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1510"><a href="#cb78-1510" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;How badly were you economically harmed by the 2015 floods?&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1511"><a href="#cb78-1511" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Distance to the 2015 flood (meters)&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1512"><a href="#cb78-1512" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method=</span>lm, <span class="at">se=</span><span class="cn">FALSE</span>, <span class="at">linetype=</span><span class="st">&quot;dashed&quot;</span>,</span>
<span id="cb78-1513"><a href="#cb78-1513" aria-hidden="true" tabindex="-1"></a>             <span class="at">color=</span><span class="st">&quot;darkred&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1514"><a href="#cb78-1514" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
<span id="cb78-1515"><a href="#cb78-1515" aria-hidden="true" tabindex="-1"></a>fig_harm_distance</span>
<span id="cb78-1516"><a href="#cb78-1516" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1517"><a href="#cb78-1517" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A13_harm_distance.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1518"><a href="#cb78-1518" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1519"><a href="#cb78-1519" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1520"><a href="#cb78-1520" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1521"><a href="#cb78-1521" aria-hidden="true" tabindex="-1"></a><span class="fu">### Figure A14: Flood exposure</span></span>
<span id="cb78-1522"><a href="#cb78-1522" aria-hidden="true" tabindex="-1"></a><span class="in">```{r flood_econ, message=FALSE, warning=FALSE, echo=FALSE,out.width=&quot;70%&quot;,fig.align=&quot;center&quot;,message=FALSE, results=&#39;hide&#39;,fig.cap=c(&quot;Distribution: seft-reported economic hardship due to the flood&quot;)}</span></span>
<span id="cb78-1523"><a href="#cb78-1523" aria-hidden="true" tabindex="-1"></a>  covariates<span class="ot">&lt;-</span>covariates <span class="sc">%&gt;%</span></span>
<span id="cb78-1524"><a href="#cb78-1524" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1525"><a href="#cb78-1525" aria-hidden="true" tabindex="-1"></a>  <span class="co"># make binary and character</span></span>
<span id="cb78-1526"><a href="#cb78-1526" aria-hidden="true" tabindex="-1"></a>  <span class="at">flood_econ_bin =</span> <span class="fu">case_when</span>(flood_econ <span class="sc">&lt;=</span><span class="dv">3</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-1527"><a href="#cb78-1527" aria-hidden="true" tabindex="-1"></a>                             flood_econ <span class="sc">&gt;</span><span class="dv">3</span> <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-1528"><a href="#cb78-1528" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb78-1529"><a href="#cb78-1529" aria-hidden="true" tabindex="-1"></a>  <span class="at">flood_econ_char =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-1530"><a href="#cb78-1530" aria-hidden="true" tabindex="-1"></a>                              flood_econ <span class="sc">==</span><span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;Not at all&quot;</span>,</span>
<span id="cb78-1531"><a href="#cb78-1531" aria-hidden="true" tabindex="-1"></a>                              flood_econ <span class="sc">==</span><span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Just mildly&quot;</span>,</span>
<span id="cb78-1532"><a href="#cb78-1532" aria-hidden="true" tabindex="-1"></a>                              flood_econ <span class="sc">==</span><span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Somewhat&quot;</span>,</span>
<span id="cb78-1533"><a href="#cb78-1533" aria-hidden="true" tabindex="-1"></a>                              flood_econ <span class="sc">==</span><span class="dv">4</span> <span class="sc">~</span> <span class="st">&quot;Very badly&quot;</span>,</span>
<span id="cb78-1534"><a href="#cb78-1534" aria-hidden="true" tabindex="-1"></a>                              flood_econ <span class="sc">==</span><span class="dv">5</span> <span class="sc">~</span> <span class="st">&quot;Extremely badly&quot;</span>)</span>
<span id="cb78-1535"><a href="#cb78-1535" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-1536"><a href="#cb78-1536" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Order  </span></span>
<span id="cb78-1537"><a href="#cb78-1537" aria-hidden="true" tabindex="-1"></a>  manual_country_order <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Not at all&quot;</span>,<span class="st">&quot;Just mildly&quot;</span>,<span class="st">&quot;Somewhat&quot;</span>,<span class="st">&quot;Very badly&quot;</span>,<span class="st">&quot;Extremely badly&quot;</span>)</span>
<span id="cb78-1538"><a href="#cb78-1538" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Apply the manual order </span></span>
<span id="cb78-1539"><a href="#cb78-1539" aria-hidden="true" tabindex="-1"></a>  covariates<span class="sc">$</span>flood_econ_char <span class="ot">&lt;-</span> <span class="fu">factor</span>(covariates<span class="sc">$</span>flood_econ_char, <span class="at">levels =</span> manual_country_order)  </span>
<span id="cb78-1540"><a href="#cb78-1540" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1541"><a href="#cb78-1541" aria-hidden="true" tabindex="-1"></a>  flood_econ<span class="ot">&lt;-</span>covariates <span class="sc">%&gt;%</span></span>
<span id="cb78-1542"><a href="#cb78-1542" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(flood_econ_char)) <span class="sc">+</span></span>
<span id="cb78-1543"><a href="#cb78-1543" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>() <span class="sc">+</span></span>
<span id="cb78-1544"><a href="#cb78-1544" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> scales<span class="sc">::</span><span class="fu">percent</span>(..prop..), <span class="at">group =</span> <span class="dv">1</span>), <span class="at">stat=</span> <span class="st">&quot;count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb78-1545"><a href="#cb78-1545" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_colour_viridis_d</span>(<span class="at">name =</span> <span class="st">&#39;S_preference&#39;</span>) <span class="sc">+</span> </span>
<span id="cb78-1546"><a href="#cb78-1546" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-1547"><a href="#cb78-1547" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;How badly were you economically harmed by the 2015 floods?&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1548"><a href="#cb78-1548" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Number of observations&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1549"><a href="#cb78-1549" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1550"><a href="#cb78-1550" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-1551"><a href="#cb78-1551" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>)</span>
<span id="cb78-1552"><a href="#cb78-1552" aria-hidden="true" tabindex="-1"></a>  flood_econ</span>
<span id="cb78-1553"><a href="#cb78-1553" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1554"><a href="#cb78-1554" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A14_flood_econ.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1555"><a href="#cb78-1555" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1556"><a href="#cb78-1556" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1557"><a href="#cb78-1557" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1558"><a href="#cb78-1558" aria-hidden="true" tabindex="-1"></a><span class="fu">##  Figure A15: Difference in AMCEs and Marginal Means</span></span>
<span id="cb78-1559"><a href="#cb78-1559" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1560"><a href="#cb78-1560" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, message=FALSE, warning=FALSE, echo=FALSE,out.width=&quot;100%&quot;,fig.align=&quot;center&quot;,message=FALSE, results=&#39;hide&#39;}</span></span>
<span id="cb78-1561"><a href="#cb78-1561" aria-hidden="true" tabindex="-1"></a>  df_long_main <span class="ot">&lt;-</span> <span class="fu">left_join</span>(df_long,covariates, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;CaseID&quot;</span>)) </span>
<span id="cb78-1562"><a href="#cb78-1562" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1563"><a href="#cb78-1563" aria-hidden="true" tabindex="-1"></a>  df_long_main<span class="ot">&lt;-</span>df_long_main <span class="sc">%&gt;%</span></span>
<span id="cb78-1564"><a href="#cb78-1564" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1565"><a href="#cb78-1565" aria-hidden="true" tabindex="-1"></a>  <span class="co"># make binary and character</span></span>
<span id="cb78-1566"><a href="#cb78-1566" aria-hidden="true" tabindex="-1"></a>  <span class="at">flood_econ_bin =</span> <span class="fu">case_when</span>(flood_econ <span class="sc">&lt;=</span><span class="dv">3</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-1567"><a href="#cb78-1567" aria-hidden="true" tabindex="-1"></a>                             flood_econ <span class="sc">&gt;</span><span class="dv">3</span> <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-1568"><a href="#cb78-1568" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb78-1569"><a href="#cb78-1569" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1570"><a href="#cb78-1570" aria-hidden="true" tabindex="-1"></a>economic<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>Ask<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span></span>
<span id="cb78-1571"><a href="#cb78-1571" aria-hidden="true" tabindex="-1"></a>                   Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span></span>
<span id="cb78-1572"><a href="#cb78-1572" aria-hidden="true" tabindex="-1"></a>                   Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span></span>
<span id="cb78-1573"><a href="#cb78-1573" aria-hidden="true" tabindex="-1"></a>                   Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span></span>
<span id="cb78-1574"><a href="#cb78-1574" aria-hidden="true" tabindex="-1"></a>                   Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span></span>
<span id="cb78-1575"><a href="#cb78-1575" aria-hidden="true" tabindex="-1"></a>                   EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span></span>
<span id="cb78-1576"><a href="#cb78-1576" aria-hidden="true" tabindex="-1"></a>                   Honesty<span class="sc">*</span>flood_econ_bin,</span>
<span id="cb78-1577"><a href="#cb78-1577" aria-hidden="true" tabindex="-1"></a>                   <span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID) <span class="sc">%&gt;%</span> <span class="fu">tidy</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb78-1578"><a href="#cb78-1578" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="fu">grepl</span>(<span class="st">&#39;:&#39;</span>, term)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-1579"><a href="#cb78-1579" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">term=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(term, </span>
<span id="cb78-1580"><a href="#cb78-1580" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effort_preventionPrevention Effort:flood_econ_bin&quot;</span><span class="ot">=</span><span class="st">&quot;Preparedness Effort&quot;</span>, </span>
<span id="cb78-1581"><a href="#cb78-1581" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effort_reliefRelief Effort:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort&quot;</span>,</span>
<span id="cb78-1582"><a href="#cb78-1582" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effective_preventionPrevention Effective:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effevtive&quot;</span>,</span>
<span id="cb78-1583"><a href="#cb78-1583" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effective_reliefRelief Effective:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effevtive&quot;</span>,</span>
<span id="cb78-1584"><a href="#cb78-1584" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;AskAsk Relief:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Ask Help&quot;</span>,</span>
<span id="cb78-1585"><a href="#cb78-1585" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EQVisits:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb78-1586"><a href="#cb78-1586" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;HonestyCorruption:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span>,</span>
<span id="cb78-1587"><a href="#cb78-1587" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;HonestyVote Buying:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying&quot;</span></span>
<span id="cb78-1588"><a href="#cb78-1588" aria-hidden="true" tabindex="-1"></a>                     )</span>
<span id="cb78-1589"><a href="#cb78-1589" aria-hidden="true" tabindex="-1"></a>         )<span class="sc">%&gt;%</span> </span>
<span id="cb78-1590"><a href="#cb78-1590" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1591"><a href="#cb78-1591" aria-hidden="true" tabindex="-1"></a>  <span class="at">group =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-1592"><a href="#cb78-1592" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Preparedness Effort&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effort&quot;</span>,</span>
<span id="cb78-1593"><a href="#cb78-1593" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Relief Effort&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effort&quot;</span>,</span>
<span id="cb78-1594"><a href="#cb78-1594" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Preparedness Effevtive&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effevtive&quot;</span>,</span>
<span id="cb78-1595"><a href="#cb78-1595" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Relief Effevtive&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effevtive&quot;</span>,</span>
<span id="cb78-1596"><a href="#cb78-1596" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Ask Help&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-1597"><a href="#cb78-1597" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Visits&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-1598"><a href="#cb78-1598" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Corruption&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-1599"><a href="#cb78-1599" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Vote Buying&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1600"><a href="#cb78-1600" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-1601"><a href="#cb78-1601" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-1602"><a href="#cb78-1602" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1603"><a href="#cb78-1603" aria-hidden="true" tabindex="-1"></a>f7<span class="ot">&lt;-</span>economic<span class="sc">%&gt;%</span> </span>
<span id="cb78-1604"><a href="#cb78-1604" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">term =</span> <span class="fu">fct_reorder</span>(term, <span class="fu">desc</span>(<span class="sc">-</span>estimate))) <span class="sc">%&gt;%</span></span>
<span id="cb78-1605"><a href="#cb78-1605" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> term)) <span class="sc">+</span><span class="co">#color = as.factor(group)</span></span>
<span id="cb78-1606"><a href="#cb78-1606" aria-hidden="true" tabindex="-1"></a>      <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-1607"><a href="#cb78-1607" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> term, </span>
<span id="cb78-1608"><a href="#cb78-1608" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span>conf.low, </span>
<span id="cb78-1609"><a href="#cb78-1609" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> conf.high),</span>
<span id="cb78-1610"><a href="#cb78-1610" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-1611"><a href="#cb78-1611" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_colour_viridis_d(name = &#39;Group&#39;) + </span></span>
<span id="cb78-1612"><a href="#cb78-1612" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-1613"><a href="#cb78-1613" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;difference in AMCEs (low vs. high losses)&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1614"><a href="#cb78-1614" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.27</span>, <span class="fl">0.2</span>))<span class="sc">+</span></span>
<span id="cb78-1615"><a href="#cb78-1615" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y=</span><span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb78-1616"><a href="#cb78-1616" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1617"><a href="#cb78-1617" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1618"><a href="#cb78-1618" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-1619"><a href="#cb78-1619" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1620"><a href="#cb78-1620" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Effect on Probability of Support for Candidate&quot;</span>) </span>
<span id="cb78-1621"><a href="#cb78-1621" aria-hidden="true" tabindex="-1"></a><span class="co"># +</span></span>
<span id="cb78-1622"><a href="#cb78-1622" aria-hidden="true" tabindex="-1"></a>    <span class="co">#theme(legend.position=&quot;bottom&quot;)</span></span>
<span id="cb78-1623"><a href="#cb78-1623" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1624"><a href="#cb78-1624" aria-hidden="true" tabindex="-1"></a>  df_long_main<span class="sc">$</span>flood_econ_bin <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df_long_main<span class="sc">$</span>flood_econ_bin) </span>
<span id="cb78-1625"><a href="#cb78-1625" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1626"><a href="#cb78-1626" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1627"><a href="#cb78-1627" aria-hidden="true" tabindex="-1"></a>f9<span class="ot">&lt;-</span><span class="fu">cj</span>(df_long_main, Choice<span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>Ask<span class="sc">+</span>EQ<span class="sc">+</span>Honesty, <span class="at">id =</span> <span class="sc">~</span>CaseID, <span class="at">estimate =</span> <span class="st">&quot;mm_differences&quot;</span>, <span class="at">by =</span> <span class="sc">~</span>flood_econ_bin)<span class="sc">%&gt;%</span> </span>
<span id="cb78-1628"><a href="#cb78-1628" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">level =</span> <span class="fu">fct_reorder</span>(level, <span class="fu">desc</span>(<span class="sc">-</span>estimate))) <span class="sc">%&gt;%</span></span>
<span id="cb78-1629"><a href="#cb78-1629" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> level)) <span class="sc">+</span><span class="co"># color = as.factor(group)</span></span>
<span id="cb78-1630"><a href="#cb78-1630" aria-hidden="true" tabindex="-1"></a>      <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-1631"><a href="#cb78-1631" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> level, </span>
<span id="cb78-1632"><a href="#cb78-1632" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span>lower, </span>
<span id="cb78-1633"><a href="#cb78-1633" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> upper),</span>
<span id="cb78-1634"><a href="#cb78-1634" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-1635"><a href="#cb78-1635" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_colour_viridis_d</span>(<span class="at">name =</span> <span class="st">&#39;Group&#39;</span>) <span class="sc">+</span> </span>
<span id="cb78-1636"><a href="#cb78-1636" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-1637"><a href="#cb78-1637" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;difference in marginal means (low vs. high losses)&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1638"><a href="#cb78-1638" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.27</span>, <span class="fl">0.2</span>))<span class="sc">+</span></span>
<span id="cb78-1639"><a href="#cb78-1639" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y=</span><span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb78-1640"><a href="#cb78-1640" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1641"><a href="#cb78-1641" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1642"><a href="#cb78-1642" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-1643"><a href="#cb78-1643" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1644"><a href="#cb78-1644" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Effect on Probability of Support for Candidate&quot;</span>) </span>
<span id="cb78-1645"><a href="#cb78-1645" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1646"><a href="#cb78-1646" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1647"><a href="#cb78-1647" aria-hidden="true" tabindex="-1"></a>fig_econ  <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(f7,f9,</span>
<span id="cb78-1648"><a href="#cb78-1648" aria-hidden="true" tabindex="-1"></a>          <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>)</span>
<span id="cb78-1649"><a href="#cb78-1649" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1650"><a href="#cb78-1650" aria-hidden="true" tabindex="-1"></a>fig_econ</span>
<span id="cb78-1651"><a href="#cb78-1651" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A15_econ.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1652"><a href="#cb78-1652" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1653"><a href="#cb78-1653" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1654"><a href="#cb78-1654" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1655"><a href="#cb78-1655" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A16: Balance Tests</span></span>
<span id="cb78-1656"><a href="#cb78-1656" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1657"><a href="#cb78-1657" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, message=FALSE, warning=FALSE, echo=FALSE,out.width=&quot;100%&quot;,fig.align=&quot;center&quot;,message=FALSE, results=&#39;hide&#39;}</span></span>
<span id="cb78-1658"><a href="#cb78-1658" aria-hidden="true" tabindex="-1"></a><span class="do">###################</span></span>
<span id="cb78-1659"><a href="#cb78-1659" aria-hidden="true" tabindex="-1"></a><span class="co"># Balance Check</span></span>
<span id="cb78-1660"><a href="#cb78-1660" aria-hidden="true" tabindex="-1"></a><span class="do">###################</span></span>
<span id="cb78-1661"><a href="#cb78-1661" aria-hidden="true" tabindex="-1"></a><span class="co"># subset to farmer (only here the prime should work)</span></span>
<span id="cb78-1662"><a href="#cb78-1662" aria-hidden="true" tabindex="-1"></a>total1 <span class="ot">&lt;-</span> <span class="fu">subset</span>(df_long_main, farmer<span class="sc">==</span><span class="dv">1</span>)</span>
<span id="cb78-1663"><a href="#cb78-1663" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(total1)</span>
<span id="cb78-1664"><a href="#cb78-1664" aria-hidden="true" tabindex="-1"></a>total1<span class="sc">$</span>disaster_prime[total1<span class="sc">$</span>disaster_prime<span class="sc">==</span><span class="st">&quot;control&quot;</span>] <span class="ot">&lt;-</span> <span class="dv">0</span></span>
<span id="cb78-1665"><a href="#cb78-1665" aria-hidden="true" tabindex="-1"></a>total1<span class="sc">$</span>disaster_prime[total1<span class="sc">$</span>disaster_prime<span class="sc">==</span><span class="st">&quot;treatment&quot;</span>] <span class="ot">&lt;-</span> <span class="dv">1</span></span>
<span id="cb78-1666"><a href="#cb78-1666" aria-hidden="true" tabindex="-1"></a><span class="co">#total1$gender[total1$gender==&quot;Female&quot;] &lt;- 0</span></span>
<span id="cb78-1667"><a href="#cb78-1667" aria-hidden="true" tabindex="-1"></a><span class="co">#total1$gender[total1$gender==&quot;Male&quot;] &lt;- 1</span></span>
<span id="cb78-1668"><a href="#cb78-1668" aria-hidden="true" tabindex="-1"></a>total1<span class="ot">&lt;-</span>total1[<span class="sc">!</span>(total1<span class="sc">$</span>gender<span class="sc">&gt;</span><span class="dv">1</span>),]</span>
<span id="cb78-1669"><a href="#cb78-1669" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(total1<span class="sc">$</span>gender)</span>
<span id="cb78-1670"><a href="#cb78-1670" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1671"><a href="#cb78-1671" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1672"><a href="#cb78-1672" aria-hidden="true" tabindex="-1"></a><span class="co"># select variables </span></span>
<span id="cb78-1673"><a href="#cb78-1673" aria-hidden="true" tabindex="-1"></a>myvars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;disaster_prime&quot;</span>, <span class="st">&quot;education&quot;</span>, <span class="st">&quot;income&quot;</span>, <span class="st">&quot;age&quot;</span>,<span class="st">&quot;gender&quot;</span>,<span class="st">&quot;interested_politics&quot;</span>,<span class="st">&quot;trust_MP&quot;</span>)</span>
<span id="cb78-1674"><a href="#cb78-1674" aria-hidden="true" tabindex="-1"></a>newdata <span class="ot">&lt;-</span> total1[myvars]</span>
<span id="cb78-1675"><a href="#cb78-1675" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1676"><a href="#cb78-1676" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1677"><a href="#cb78-1677" aria-hidden="true" tabindex="-1"></a><span class="co"># Modify each plot to include a legend with a common title</span></span>
<span id="cb78-1678"><a href="#cb78-1678" aria-hidden="true" tabindex="-1"></a>education <span class="ot">&lt;-</span> <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-1679"><a href="#cb78-1679" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_density</span>(<span class="at">data =</span> newdata, <span class="fu">aes</span>(<span class="at">x =</span> education, <span class="at">fill =</span> <span class="fu">factor</span>(disaster_prime, <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Control&quot;</span>, <span class="st">&quot;Treatment&quot;</span>))), </span>
<span id="cb78-1680"><a href="#cb78-1680" aria-hidden="true" tabindex="-1"></a>               <span class="at">alpha =</span> <span class="fl">0.2</span>, <span class="at">adjust =</span> <span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb78-1681"><a href="#cb78-1681" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Education&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1682"><a href="#cb78-1682" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Density&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1683"><a href="#cb78-1683" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_classic</span>() <span class="sc">+</span> </span>
<span id="cb78-1684"><a href="#cb78-1684" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_grey</span>() <span class="sc">+</span></span>
<span id="cb78-1685"><a href="#cb78-1685" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>),</span>
<span id="cb78-1686"><a href="#cb78-1686" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">14</span>)) <span class="sc">+</span></span>
<span id="cb78-1687"><a href="#cb78-1687" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">fill =</span> <span class="st">&quot;Treatment Group&quot;</span>)</span>
<span id="cb78-1688"><a href="#cb78-1688" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1689"><a href="#cb78-1689" aria-hidden="true" tabindex="-1"></a>age <span class="ot">&lt;-</span> <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-1690"><a href="#cb78-1690" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_density</span>(<span class="at">data =</span> newdata, <span class="fu">aes</span>(<span class="at">x =</span> age, <span class="at">fill =</span> <span class="fu">factor</span>(disaster_prime, <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Control&quot;</span>, <span class="st">&quot;Treatment&quot;</span>))), </span>
<span id="cb78-1691"><a href="#cb78-1691" aria-hidden="true" tabindex="-1"></a>               <span class="at">alpha =</span> <span class="fl">0.2</span>, <span class="at">adjust =</span> <span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb78-1692"><a href="#cb78-1692" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Age&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1693"><a href="#cb78-1693" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Density&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1694"><a href="#cb78-1694" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_classic</span>() <span class="sc">+</span> </span>
<span id="cb78-1695"><a href="#cb78-1695" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_grey</span>() <span class="sc">+</span></span>
<span id="cb78-1696"><a href="#cb78-1696" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>),</span>
<span id="cb78-1697"><a href="#cb78-1697" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">14</span>)) <span class="sc">+</span></span>
<span id="cb78-1698"><a href="#cb78-1698" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">fill =</span> <span class="st">&quot;Treatment Group&quot;</span>)</span>
<span id="cb78-1699"><a href="#cb78-1699" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1700"><a href="#cb78-1700" aria-hidden="true" tabindex="-1"></a>income <span class="ot">&lt;-</span> <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-1701"><a href="#cb78-1701" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_density</span>(<span class="at">data =</span> newdata, <span class="fu">aes</span>(<span class="at">x =</span> income, <span class="at">fill =</span> <span class="fu">factor</span>(disaster_prime, <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Control&quot;</span>, <span class="st">&quot;Treatment&quot;</span>))), </span>
<span id="cb78-1702"><a href="#cb78-1702" aria-hidden="true" tabindex="-1"></a>               <span class="at">alpha =</span> <span class="fl">0.2</span>, <span class="at">adjust =</span> <span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb78-1703"><a href="#cb78-1703" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Income&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1704"><a href="#cb78-1704" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Density&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1705"><a href="#cb78-1705" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_classic</span>() <span class="sc">+</span> </span>
<span id="cb78-1706"><a href="#cb78-1706" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_grey</span>() <span class="sc">+</span></span>
<span id="cb78-1707"><a href="#cb78-1707" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>),</span>
<span id="cb78-1708"><a href="#cb78-1708" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">14</span>)) <span class="sc">+</span></span>
<span id="cb78-1709"><a href="#cb78-1709" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">fill =</span> <span class="st">&quot;Treatment Group&quot;</span>)</span>
<span id="cb78-1710"><a href="#cb78-1710" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1711"><a href="#cb78-1711" aria-hidden="true" tabindex="-1"></a>gender <span class="ot">&lt;-</span> <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-1712"><a href="#cb78-1712" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_density</span>(<span class="at">data =</span> newdata, <span class="fu">aes</span>(<span class="at">x =</span> gender, <span class="at">fill =</span> <span class="fu">factor</span>(disaster_prime, <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Control&quot;</span>, <span class="st">&quot;Treatment&quot;</span>))), </span>
<span id="cb78-1713"><a href="#cb78-1713" aria-hidden="true" tabindex="-1"></a>               <span class="at">alpha =</span> <span class="fl">0.2</span>, <span class="at">adjust =</span> <span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb78-1714"><a href="#cb78-1714" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Gender&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1715"><a href="#cb78-1715" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Density&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-1716"><a href="#cb78-1716" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_classic</span>() <span class="sc">+</span> </span>
<span id="cb78-1717"><a href="#cb78-1717" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_grey</span>() <span class="sc">+</span></span>
<span id="cb78-1718"><a href="#cb78-1718" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>),</span>
<span id="cb78-1719"><a href="#cb78-1719" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">14</span>)) <span class="sc">+</span></span>
<span id="cb78-1720"><a href="#cb78-1720" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">fill =</span> <span class="st">&quot;Treatment Group&quot;</span>)</span>
<span id="cb78-1721"><a href="#cb78-1721" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1722"><a href="#cb78-1722" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the plots into a grid with a common legend</span></span>
<span id="cb78-1723"><a href="#cb78-1723" aria-hidden="true" tabindex="-1"></a>balance <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(education, income, age, gender,</span>
<span id="cb78-1724"><a href="#cb78-1724" aria-hidden="true" tabindex="-1"></a>                     <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">2</span>, <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend =</span> <span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-1725"><a href="#cb78-1725" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1726"><a href="#cb78-1726" aria-hidden="true" tabindex="-1"></a><span class="co"># Display the combined plot</span></span>
<span id="cb78-1727"><a href="#cb78-1727" aria-hidden="true" tabindex="-1"></a>balance</span>
<span id="cb78-1728"><a href="#cb78-1728" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1729"><a href="#cb78-1729" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A16_balance.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1730"><a href="#cb78-1730" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1731"><a href="#cb78-1731" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1732"><a href="#cb78-1732" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1733"><a href="#cb78-1733" aria-hidden="true" tabindex="-1"></a><span class="fu">## Table A8: Manipulation Check</span></span>
<span id="cb78-1734"><a href="#cb78-1734" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1735"><a href="#cb78-1735" aria-hidden="true" tabindex="-1"></a><span class="in">```{r ,results = &#39;asis&#39;}</span></span>
<span id="cb78-1736"><a href="#cb78-1736" aria-hidden="true" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;1_input/covariates.rds&quot;</span>)</span>
<span id="cb78-1737"><a href="#cb78-1737" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1738"><a href="#cb78-1738" aria-hidden="true" tabindex="-1"></a>manipulation<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(manipulation <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb78-1739"><a href="#cb78-1739" aria-hidden="true" tabindex="-1"></a>resp_MP<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(T_q37_4 <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb78-1740"><a href="#cb78-1740" aria-hidden="true" tabindex="-1"></a>resp_MP2<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(T_q38_4 <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb78-1741"><a href="#cb78-1741" aria-hidden="true" tabindex="-1"></a>worried<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(worried <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb78-1742"><a href="#cb78-1742" aria-hidden="true" tabindex="-1"></a>hopeless<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(T_q24_4 <span class="sc">~</span>disaster_prime,<span class="at">data=</span>covariates)</span>
<span id="cb78-1743"><a href="#cb78-1743" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1744"><a href="#cb78-1744" aria-hidden="true" tabindex="-1"></a><span class="co"># Table </span></span>
<span id="cb78-1745"><a href="#cb78-1745" aria-hidden="true" tabindex="-1"></a>tex_table <span class="ot">&lt;-</span> <span class="fu">texreg</span>(</span>
<span id="cb78-1746"><a href="#cb78-1746" aria-hidden="true" tabindex="-1"></a>  <span class="fu">list</span>(manipulation, resp_MP, resp_MP2, worried, hopeless),</span>
<span id="cb78-1747"><a href="#cb78-1747" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.ci =</span> <span class="cn">FALSE</span>,</span>
<span id="cb78-1748"><a href="#cb78-1748" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="fu">c</span>(<span class="fl">0.001</span>, <span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb78-1749"><a href="#cb78-1749" aria-hidden="true" tabindex="-1"></a>  <span class="at">caption =</span> <span class="st">&quot;Manipulation Check&quot;</span>,</span>
<span id="cb78-1750"><a href="#cb78-1750" aria-hidden="true" tabindex="-1"></a>  <span class="at">custom.model.names =</span> <span class="fu">c</span>(<span class="st">&quot;Financial Worry&quot;</span>, <span class="st">&quot;MP Responsible Relief&quot;</span>, </span>
<span id="cb78-1751"><a href="#cb78-1751" aria-hidden="true" tabindex="-1"></a>                         <span class="st">&quot;MP Responsible Preparedness&quot;</span>, <span class="st">&quot;Flood Worry&quot;</span>, <span class="st">&quot;Hopeless&quot;</span>)</span>
<span id="cb78-1752"><a href="#cb78-1752" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1753"><a href="#cb78-1753" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1754"><a href="#cb78-1754" aria-hidden="true" tabindex="-1"></a><span class="co"># Write the LaTeX table to a file</span></span>
<span id="cb78-1755"><a href="#cb78-1755" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(tex_table, <span class="at">file =</span> <span class="st">&quot;2_output/tab_A8_manipulation_check.tex&quot;</span>)</span>
<span id="cb78-1756"><a href="#cb78-1756" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1757"><a href="#cb78-1757" aria-hidden="true" tabindex="-1"></a><span class="fu">htmlreg</span>(</span>
<span id="cb78-1758"><a href="#cb78-1758" aria-hidden="true" tabindex="-1"></a>  <span class="fu">list</span>(manipulation, resp_MP, resp_MP2, worried, hopeless),</span>
<span id="cb78-1759"><a href="#cb78-1759" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.ci =</span> <span class="cn">FALSE</span>,</span>
<span id="cb78-1760"><a href="#cb78-1760" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="fu">c</span>(<span class="fl">0.001</span>, <span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb78-1761"><a href="#cb78-1761" aria-hidden="true" tabindex="-1"></a>  <span class="at">caption =</span> <span class="st">&quot;Manipulation Check&quot;</span>,</span>
<span id="cb78-1762"><a href="#cb78-1762" aria-hidden="true" tabindex="-1"></a>  <span class="at">custom.model.names =</span> <span class="fu">c</span>(<span class="st">&quot;Financial Worry&quot;</span>, <span class="st">&quot;MP Responsible Relief&quot;</span>, </span>
<span id="cb78-1763"><a href="#cb78-1763" aria-hidden="true" tabindex="-1"></a>                         <span class="st">&quot;MP Responsible Preparedness&quot;</span>, <span class="st">&quot;Flood Worry&quot;</span>, <span class="st">&quot;Hopeless&quot;</span>)</span>
<span id="cb78-1764"><a href="#cb78-1764" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1765"><a href="#cb78-1765" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1766"><a href="#cb78-1766" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1767"><a href="#cb78-1767" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1768"><a href="#cb78-1768" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1769"><a href="#cb78-1769" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1770"><a href="#cb78-1770" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A17: Difference in AMCEs of Attributes on Candidate Support, by disaster prime</span></span>
<span id="cb78-1771"><a href="#cb78-1771" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1772"><a href="#cb78-1772" aria-hidden="true" tabindex="-1"></a><span class="in">```{r ,results = &#39;asis&#39;,warning=FALSE}</span></span>
<span id="cb78-1773"><a href="#cb78-1773" aria-hidden="true" tabindex="-1"></a>  df_long_main <span class="ot">&lt;-</span> <span class="fu">left_join</span>(df_long,covariates, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;CaseID&quot;</span>)) </span>
<span id="cb78-1774"><a href="#cb78-1774" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1775"><a href="#cb78-1775" aria-hidden="true" tabindex="-1"></a><span class="co"># Prime</span></span>
<span id="cb78-1776"><a href="#cb78-1776" aria-hidden="true" tabindex="-1"></a>prime<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>Ask<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>disaster_prime<span class="sc">+</span>Effort_prevention<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb78-1777"><a href="#cb78-1777" aria-hidden="true" tabindex="-1"></a>                   Effort_relief<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb78-1778"><a href="#cb78-1778" aria-hidden="true" tabindex="-1"></a>                   Effective_prevention<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb78-1779"><a href="#cb78-1779" aria-hidden="true" tabindex="-1"></a>                   Effective_relief<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb78-1780"><a href="#cb78-1780" aria-hidden="true" tabindex="-1"></a>                   Ask<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb78-1781"><a href="#cb78-1781" aria-hidden="true" tabindex="-1"></a>                   EQ<span class="sc">*</span>disaster_prime<span class="sc">+</span></span>
<span id="cb78-1782"><a href="#cb78-1782" aria-hidden="true" tabindex="-1"></a>                   Honesty<span class="sc">*</span>disaster_prime,</span>
<span id="cb78-1783"><a href="#cb78-1783" aria-hidden="true" tabindex="-1"></a>                   <span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID) <span class="sc">%&gt;%</span> <span class="fu">tidy</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb78-1784"><a href="#cb78-1784" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(<span class="fu">grepl</span>(<span class="st">&#39;:&#39;</span>, term)) <span class="sc">%&gt;%</span> </span>
<span id="cb78-1785"><a href="#cb78-1785" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">term=</span>dplyr<span class="sc">::</span><span class="fu">recode</span>(term, </span>
<span id="cb78-1786"><a href="#cb78-1786" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effort_preventionPrevention Effort:disaster_primetreatment&quot;</span><span class="ot">=</span><span class="st">&quot;Preparedness Effort&quot;</span>, </span>
<span id="cb78-1787"><a href="#cb78-1787" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effort_reliefRelief Effort:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort&quot;</span>,</span>
<span id="cb78-1788"><a href="#cb78-1788" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effective_preventionPrevention Effective:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Preparedness Effevtive&quot;</span>,</span>
<span id="cb78-1789"><a href="#cb78-1789" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;Effective_reliefRelief Effective:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effevtive&quot;</span>,</span>
<span id="cb78-1790"><a href="#cb78-1790" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;AskAsk Relief:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Ask Help&quot;</span>,</span>
<span id="cb78-1791"><a href="#cb78-1791" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;EQVisits:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits&quot;</span>,</span>
<span id="cb78-1792"><a href="#cb78-1792" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;HonestyCorruption:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption&quot;</span>,</span>
<span id="cb78-1793"><a href="#cb78-1793" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&quot;HonestyVote Buying:disaster_primetreatment&quot;</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying&quot;</span></span>
<span id="cb78-1794"><a href="#cb78-1794" aria-hidden="true" tabindex="-1"></a>                     )</span>
<span id="cb78-1795"><a href="#cb78-1795" aria-hidden="true" tabindex="-1"></a>         )<span class="sc">%&gt;%</span> </span>
<span id="cb78-1796"><a href="#cb78-1796" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1797"><a href="#cb78-1797" aria-hidden="true" tabindex="-1"></a>  <span class="at">group =</span> <span class="fu">case_when</span>(</span>
<span id="cb78-1798"><a href="#cb78-1798" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Preparedness Effort&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effort&quot;</span>,</span>
<span id="cb78-1799"><a href="#cb78-1799" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Relief Effort&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effort&quot;</span>,</span>
<span id="cb78-1800"><a href="#cb78-1800" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Preparedness Effevtive&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effevtive&quot;</span>,</span>
<span id="cb78-1801"><a href="#cb78-1801" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Relief Effevtive&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Effevtive&quot;</span>,</span>
<span id="cb78-1802"><a href="#cb78-1802" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Ask Help&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-1803"><a href="#cb78-1803" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Visits&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-1804"><a href="#cb78-1804" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Corruption&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span>,</span>
<span id="cb78-1805"><a href="#cb78-1805" aria-hidden="true" tabindex="-1"></a>  term <span class="sc">==</span> <span class="st">&quot;Vote Buying&quot;</span>  <span class="sc">~</span> <span class="st">&quot;Other&quot;</span></span>
<span id="cb78-1806"><a href="#cb78-1806" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-1807"><a href="#cb78-1807" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb78-1808"><a href="#cb78-1808" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1809"><a href="#cb78-1809" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1810"><a href="#cb78-1810" aria-hidden="true" tabindex="-1"></a>fig_prime<span class="ot">&lt;-</span>prime<span class="sc">%&gt;%</span> </span>
<span id="cb78-1811"><a href="#cb78-1811" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">term =</span> <span class="fu">fct_reorder</span>(term, <span class="fu">desc</span>(<span class="sc">-</span>estimate))) <span class="sc">%&gt;%</span></span>
<span id="cb78-1812"><a href="#cb78-1812" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> estimate, <span class="at">y =</span> term)) <span class="sc">+</span><span class="co"># color = as.factor(group)</span></span>
<span id="cb78-1813"><a href="#cb78-1813" aria-hidden="true" tabindex="-1"></a>      <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span> </span>
<span id="cb78-1814"><a href="#cb78-1814" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbarh</span>(<span class="fu">aes</span>(<span class="at">y =</span> term, </span>
<span id="cb78-1815"><a href="#cb78-1815" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmin =</span>conf.low, </span>
<span id="cb78-1816"><a href="#cb78-1816" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xmax =</span> conf.high),</span>
<span id="cb78-1817"><a href="#cb78-1817" aria-hidden="true" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>, <span class="at">height =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb78-1818"><a href="#cb78-1818" aria-hidden="true" tabindex="-1"></a>  <span class="co">#scale_colour_viridis_d(name = &#39;Group&#39;) + </span></span>
<span id="cb78-1819"><a href="#cb78-1819" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb78-1820"><a href="#cb78-1820" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Disaster Prime&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1821"><a href="#cb78-1821" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.27</span>, <span class="fl">0.2</span>))<span class="sc">+</span></span>
<span id="cb78-1822"><a href="#cb78-1822" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y=</span><span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb78-1823"><a href="#cb78-1823" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1824"><a href="#cb78-1824" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)) <span class="sc">+</span></span>
<span id="cb78-1825"><a href="#cb78-1825" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">linetype=</span><span class="st">&quot;dotted&quot;</span>, </span>
<span id="cb78-1826"><a href="#cb78-1826" aria-hidden="true" tabindex="-1"></a>                <span class="at">color =</span> <span class="st">&quot;red&quot;</span>)</span>
<span id="cb78-1827"><a href="#cb78-1827" aria-hidden="true" tabindex="-1"></a>fig_prime</span>
<span id="cb78-1828"><a href="#cb78-1828" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A17_prime.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-1829"><a href="#cb78-1829" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1830"><a href="#cb78-1830" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1831"><a href="#cb78-1831" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1832"><a href="#cb78-1832" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1833"><a href="#cb78-1833" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1834"><a href="#cb78-1834" aria-hidden="true" tabindex="-1"></a><span class="fu">## Table A9: Effect of Economic Losses, Full Models, with Controls</span></span>
<span id="cb78-1835"><a href="#cb78-1835" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1836"><a href="#cb78-1836" aria-hidden="true" tabindex="-1"></a><span class="in">```{r ,results = &#39;asis&#39;,warning=FALSE}</span></span>
<span id="cb78-1837"><a href="#cb78-1837" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1838"><a href="#cb78-1838" aria-hidden="true" tabindex="-1"></a>  df_long_main <span class="ot">&lt;-</span> <span class="fu">left_join</span>(df_long,covariates, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;CaseID&quot;</span>)) </span>
<span id="cb78-1839"><a href="#cb78-1839" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1840"><a href="#cb78-1840" aria-hidden="true" tabindex="-1"></a>  df_long_main<span class="ot">&lt;-</span>df_long_main <span class="sc">%&gt;%</span></span>
<span id="cb78-1841"><a href="#cb78-1841" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb78-1842"><a href="#cb78-1842" aria-hidden="true" tabindex="-1"></a>  <span class="co"># make binary and character</span></span>
<span id="cb78-1843"><a href="#cb78-1843" aria-hidden="true" tabindex="-1"></a>  <span class="at">flood_econ_bin =</span> <span class="fu">case_when</span>(flood_econ <span class="sc">&lt;=</span><span class="dv">3</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb78-1844"><a href="#cb78-1844" aria-hidden="true" tabindex="-1"></a>                             flood_econ <span class="sc">&gt;</span><span class="dv">3</span> <span class="sc">~</span> <span class="dv">1</span></span>
<span id="cb78-1845"><a href="#cb78-1845" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb78-1846"><a href="#cb78-1846" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb78-1847"><a href="#cb78-1847" aria-hidden="true" tabindex="-1"></a><span class="co"># Economic Distress</span></span>
<span id="cb78-1848"><a href="#cb78-1848" aria-hidden="true" tabindex="-1"></a>econ_distress1<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb78-1849"><a href="#cb78-1849" aria-hidden="true" tabindex="-1"></a><span class="co"># controlling for poverty</span></span>
<span id="cb78-1850"><a href="#cb78-1850" aria-hidden="true" tabindex="-1"></a>econ_distress2<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb78-1851"><a href="#cb78-1851" aria-hidden="true" tabindex="-1"></a><span class="co"># controlling for help recieved</span></span>
<span id="cb78-1852"><a href="#cb78-1852" aria-hidden="true" tabindex="-1"></a>econ_distress3<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb78-1853"><a href="#cb78-1853" aria-hidden="true" tabindex="-1"></a><span class="co"># controlling for satisfied with help </span></span>
<span id="cb78-1854"><a href="#cb78-1854" aria-hidden="true" tabindex="-1"></a>econ_distress3<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb78-1855"><a href="#cb78-1855" aria-hidden="true" tabindex="-1"></a><span class="co"># standard covariates</span></span>
<span id="cb78-1856"><a href="#cb78-1856" aria-hidden="true" tabindex="-1"></a>econ_distress4<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty<span class="sc">+</span>education<span class="sc">+</span>age<span class="sc">+</span>gender<span class="sc">+</span>interested_politics<span class="sc">+</span>trust_MP,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb78-1857"><a href="#cb78-1857" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1858"><a href="#cb78-1858" aria-hidden="true" tabindex="-1"></a>econ_distress5<span class="ot">&lt;-</span><span class="fu">lm_robust</span>(Choice <span class="sc">~</span>Effort_prevention<span class="sc">+</span>Effort_relief<span class="sc">+</span>Ask<span class="sc">+</span>Effective_prevention<span class="sc">+</span>Effective_relief<span class="sc">+</span>EQ<span class="sc">+</span>Honesty<span class="sc">+</span>flood_econ_bin<span class="sc">+</span>Effort_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effort_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Ask<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_prevention<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Effective_relief<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>EQ<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>Honesty<span class="sc">*</span>flood_econ_bin<span class="sc">+</span>poverty<span class="sc">+</span>education<span class="sc">+</span>age<span class="sc">+</span>gender<span class="sc">+</span>interested_politics<span class="sc">+</span>trust_MP<span class="sc">+</span>worried<span class="sc">+</span>personal_help<span class="sc">+</span>poverty<span class="sc">*</span>flood_econ_bin,<span class="at">data=</span>df_long_main,<span class="at">clusters =</span> CaseID)</span>
<span id="cb78-1859"><a href="#cb78-1859" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1860"><a href="#cb78-1860" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1861"><a href="#cb78-1861" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the custom coefficient names for renaming and reordering</span></span>
<span id="cb78-1862"><a href="#cb78-1862" aria-hidden="true" tabindex="-1"></a>coef_names <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb78-1863"><a href="#cb78-1863" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Effort_preventionPrevention Effort:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Prevention Effort x Economic Losses&quot;</span>,</span>
<span id="cb78-1864"><a href="#cb78-1864" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Effort_reliefRelief Effort:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effort x Economic Losses&quot;</span>,</span>
<span id="cb78-1865"><a href="#cb78-1865" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;AskAsk Relief:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Ask x Economic Losses&quot;</span>,</span>
<span id="cb78-1866"><a href="#cb78-1866" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Effective_preventionPrevention Effective:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Prevention Effective x Economic Losses&quot;</span>,</span>
<span id="cb78-1867"><a href="#cb78-1867" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Effective_reliefRelief Effective:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Relief Effective x Economic Losses&quot;</span>,</span>
<span id="cb78-1868"><a href="#cb78-1868" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;EQVisits:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Visits x Economic Losses&quot;</span>,</span>
<span id="cb78-1869"><a href="#cb78-1869" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;HonestyCorruption:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Corruption x Economic Losses&quot;</span>,</span>
<span id="cb78-1870"><a href="#cb78-1870" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;HonestyVote Buying:flood_econ_bin&quot;</span> <span class="ot">=</span> <span class="st">&quot;Vote Buying x Economic Losses&quot;</span>,</span>
<span id="cb78-1871"><a href="#cb78-1871" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;poverty&quot;</span> <span class="ot">=</span> <span class="st">&quot;Poverty&quot;</span>,</span>
<span id="cb78-1872"><a href="#cb78-1872" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;education&quot;</span> <span class="ot">=</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb78-1873"><a href="#cb78-1873" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;age&quot;</span> <span class="ot">=</span> <span class="st">&quot;Age&quot;</span>,</span>
<span id="cb78-1874"><a href="#cb78-1874" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;gender&quot;</span> <span class="ot">=</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb78-1875"><a href="#cb78-1875" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;interested_politics&quot;</span> <span class="ot">=</span> <span class="st">&quot;Interested Politics&quot;</span>,</span>
<span id="cb78-1876"><a href="#cb78-1876" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;trust_MP&quot;</span> <span class="ot">=</span> <span class="st">&quot;Trust MP&quot;</span>,</span>
<span id="cb78-1877"><a href="#cb78-1877" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;worried&quot;</span> <span class="ot">=</span> <span class="st">&quot;Flood Worried&quot;</span>,</span>
<span id="cb78-1878"><a href="#cb78-1878" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;personal_help&quot;</span> <span class="ot">=</span> <span class="st">&quot;Received Help&quot;</span>,</span>
<span id="cb78-1879"><a href="#cb78-1879" aria-hidden="true" tabindex="-1"></a>   <span class="st">&quot;flood_econ_bin:poverty&quot;</span> <span class="ot">=</span> <span class="st">&quot;Economic Losses x Poverty&quot;</span></span>
<span id="cb78-1880"><a href="#cb78-1880" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1881"><a href="#cb78-1881" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1882"><a href="#cb78-1882" aria-hidden="true" tabindex="-1"></a><span class="co"># Create the regression table with renamed and selected coefficients</span></span>
<span id="cb78-1883"><a href="#cb78-1883" aria-hidden="true" tabindex="-1"></a>tab_econ_losses<span class="ot">&lt;-</span><span class="fu">texreg</span>(</span>
<span id="cb78-1884"><a href="#cb78-1884" aria-hidden="true" tabindex="-1"></a>  <span class="fu">list</span>(econ_distress1, econ_distress2, econ_distress3, econ_distress4, econ_distress5),</span>
<span id="cb78-1885"><a href="#cb78-1885" aria-hidden="true" tabindex="-1"></a>  <span class="at">caption =</span> <span class="st">&quot;Effect of Economic Losses, Full Models, with Controls&quot;</span>,</span>
<span id="cb78-1886"><a href="#cb78-1886" aria-hidden="true" tabindex="-1"></a>  <span class="at">custom.coef.map =</span> coef_names,</span>
<span id="cb78-1887"><a href="#cb78-1887" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.ci =</span> <span class="cn">FALSE</span>,</span>
<span id="cb78-1888"><a href="#cb78-1888" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="fu">c</span>(<span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb78-1889"><a href="#cb78-1889" aria-hidden="true" tabindex="-1"></a>  <span class="at">dcolumn =</span> <span class="cn">FALSE</span>,</span>
<span id="cb78-1890"><a href="#cb78-1890" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.adjrs =</span> <span class="cn">FALSE</span>,</span>
<span id="cb78-1891"><a href="#cb78-1891" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.bic =</span> <span class="cn">TRUE</span>,</span>
<span id="cb78-1892"><a href="#cb78-1892" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.rs =</span> <span class="cn">TRUE</span>,</span>
<span id="cb78-1893"><a href="#cb78-1893" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.rmse =</span> <span class="cn">FALSE</span></span>
<span id="cb78-1894"><a href="#cb78-1894" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1895"><a href="#cb78-1895" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1896"><a href="#cb78-1896" aria-hidden="true" tabindex="-1"></a><span class="co"># Write the LaTeX table to a file</span></span>
<span id="cb78-1897"><a href="#cb78-1897" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(tab_econ_losses, <span class="at">file =</span> <span class="st">&quot;2_output/tab_A9_econ_losses.tex&quot;</span>)</span>
<span id="cb78-1898"><a href="#cb78-1898" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1899"><a href="#cb78-1899" aria-hidden="true" tabindex="-1"></a><span class="fu">htmlreg</span>(</span>
<span id="cb78-1900"><a href="#cb78-1900" aria-hidden="true" tabindex="-1"></a>  <span class="fu">list</span>(econ_distress1, econ_distress2, econ_distress3, econ_distress4, econ_distress5),</span>
<span id="cb78-1901"><a href="#cb78-1901" aria-hidden="true" tabindex="-1"></a>  <span class="at">caption =</span> <span class="st">&quot;Effect of Economic Losses, Full Models, with Controls&quot;</span>,</span>
<span id="cb78-1902"><a href="#cb78-1902" aria-hidden="true" tabindex="-1"></a>  <span class="at">custom.coef.map =</span> coef_names,</span>
<span id="cb78-1903"><a href="#cb78-1903" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.ci =</span> <span class="cn">FALSE</span>,</span>
<span id="cb78-1904"><a href="#cb78-1904" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="fu">c</span>(<span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb78-1905"><a href="#cb78-1905" aria-hidden="true" tabindex="-1"></a>  <span class="at">dcolumn =</span> <span class="cn">FALSE</span>,</span>
<span id="cb78-1906"><a href="#cb78-1906" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.adjrs =</span> <span class="cn">FALSE</span>,</span>
<span id="cb78-1907"><a href="#cb78-1907" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.bic =</span> <span class="cn">TRUE</span>,</span>
<span id="cb78-1908"><a href="#cb78-1908" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.rs =</span> <span class="cn">TRUE</span>,</span>
<span id="cb78-1909"><a href="#cb78-1909" aria-hidden="true" tabindex="-1"></a>  <span class="at">include.rmse =</span> <span class="cn">FALSE</span></span>
<span id="cb78-1910"><a href="#cb78-1910" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb78-1911"><a href="#cb78-1911" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1912"><a href="#cb78-1912" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1913"><a href="#cb78-1913" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-1914"><a href="#cb78-1914" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1915"><a href="#cb78-1915" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A18: Media coverage. 2017-2024, Source: GDELT</span></span>
<span id="cb78-1916"><a href="#cb78-1916" aria-hidden="true" tabindex="-1"></a><span class="in">```{r,warning=FALSE,message=FALSE}</span></span>
<span id="cb78-1917"><a href="#cb78-1917" aria-hidden="true" tabindex="-1"></a><span class="co"># script base on: https://rpubs.com/drkblake/1007662</span></span>
<span id="cb78-1918"><a href="#cb78-1918" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readr)</span>
<span id="cb78-1919"><a href="#cb78-1919" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1920"><a href="#cb78-1920" aria-hidden="true" tabindex="-1"></a>startdate <span class="ot">&lt;-</span> <span class="st">&quot;20170101&quot;</span></span>
<span id="cb78-1921"><a href="#cb78-1921" aria-hidden="true" tabindex="-1"></a>enddate <span class="ot">&lt;-</span> <span class="st">&quot;20231231&quot;</span></span>
<span id="cb78-1922"><a href="#cb78-1922" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-1923"><a href="#cb78-1923" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-1924"><a href="#cb78-1924" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-1925"><a href="#cb78-1925" aria-hidden="true" tabindex="-1"></a><span class="co"># 1. government </span></span>
<span id="cb78-1926"><a href="#cb78-1926" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-1927"><a href="#cb78-1927" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-1928"><a href="#cb78-1928" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-1929"><a href="#cb78-1929" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1930"><a href="#cb78-1930" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1931"><a href="#cb78-1931" aria-hidden="true" tabindex="-1"></a><span class="do">### preparedness</span></span>
<span id="cb78-1932"><a href="#cb78-1932" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;government preparedness&#39;&quot;</span></span>
<span id="cb78-1933"><a href="#cb78-1933" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-1934"><a href="#cb78-1934" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-1935"><a href="#cb78-1935" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-1936"><a href="#cb78-1936" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-1937"><a href="#cb78-1937" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-1938"><a href="#cb78-1938" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-1939"><a href="#cb78-1939" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-1940"><a href="#cb78-1940" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-1941"><a href="#cb78-1941" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1942"><a href="#cb78-1942" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-1943"><a href="#cb78-1943" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1944"><a href="#cb78-1944" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-1945"><a href="#cb78-1945" aria-hidden="true" tabindex="-1"></a>Volume_preparedness <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-1946"><a href="#cb78-1946" aria-hidden="true" tabindex="-1"></a>Volume_preparedness<span class="sc">$</span>preparednessVolume <span class="ot">&lt;-</span> Volume_preparedness<span class="sc">$</span>Value</span>
<span id="cb78-1947"><a href="#cb78-1947" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1948"><a href="#cb78-1948" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1949"><a href="#cb78-1949" aria-hidden="true" tabindex="-1"></a><span class="do">###  relief</span></span>
<span id="cb78-1950"><a href="#cb78-1950" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;government relief&#39;&quot;</span></span>
<span id="cb78-1951"><a href="#cb78-1951" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-1952"><a href="#cb78-1952" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-1953"><a href="#cb78-1953" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-1954"><a href="#cb78-1954" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-1955"><a href="#cb78-1955" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-1956"><a href="#cb78-1956" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-1957"><a href="#cb78-1957" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-1958"><a href="#cb78-1958" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-1959"><a href="#cb78-1959" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1960"><a href="#cb78-1960" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-1961"><a href="#cb78-1961" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1962"><a href="#cb78-1962" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-1963"><a href="#cb78-1963" aria-hidden="true" tabindex="-1"></a>Volume_relief <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-1964"><a href="#cb78-1964" aria-hidden="true" tabindex="-1"></a>Volume_relief<span class="sc">$</span>reliefVolume <span class="ot">&lt;-</span> Volume_relief<span class="sc">$</span>Value</span>
<span id="cb78-1965"><a href="#cb78-1965" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1966"><a href="#cb78-1966" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1967"><a href="#cb78-1967" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb78-1968"><a href="#cb78-1968" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_preparedness, Volume_relief, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb78-1969"><a href="#cb78-1969" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1970"><a href="#cb78-1970" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1971"><a href="#cb78-1971" aria-hidden="true" tabindex="-1"></a>ww_overview<span class="ot">&lt;-</span>Volume_total <span class="sc">%&gt;%</span></span>
<span id="cb78-1972"><a href="#cb78-1972" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,preparednessVolume,reliefVolume)<span class="sc">%&gt;%</span></span>
<span id="cb78-1973"><a href="#cb78-1973" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-1974"><a href="#cb78-1974" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-1975"><a href="#cb78-1975" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable)) <span class="sc">+</span></span>
<span id="cb78-1976"><a href="#cb78-1976" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-1977"><a href="#cb78-1977" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-1978"><a href="#cb78-1978" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb78-1979"><a href="#cb78-1979" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb78-1980"><a href="#cb78-1980" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb78-1981"><a href="#cb78-1981" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb78-1982"><a href="#cb78-1982" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb78-1983"><a href="#cb78-1983" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-1984"><a href="#cb78-1984" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;government preparedness&quot;</span>, <span class="st">&quot;government relief&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-1985"><a href="#cb78-1985" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, world-wide&quot;</span>)</span>
<span id="cb78-1986"><a href="#cb78-1986" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1987"><a href="#cb78-1987" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1988"><a href="#cb78-1988" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-1989"><a href="#cb78-1989" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-1990"><a href="#cb78-1990" aria-hidden="true" tabindex="-1"></a><span class="co"># Malawi MI</span></span>
<span id="cb78-1991"><a href="#cb78-1991" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-1992"><a href="#cb78-1992" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-1993"><a href="#cb78-1993" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-1994"><a href="#cb78-1994" aria-hidden="true" tabindex="-1"></a><span class="do">###  preparedness</span></span>
<span id="cb78-1995"><a href="#cb78-1995" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;government preparedness&#39; SourceCountry:MI&quot;</span></span>
<span id="cb78-1996"><a href="#cb78-1996" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-1997"><a href="#cb78-1997" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-1998"><a href="#cb78-1998" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=TimelineVol&amp;startdatetime=&quot;</span></span>
<span id="cb78-1999"><a href="#cb78-1999" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2000"><a href="#cb78-2000" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2001"><a href="#cb78-2001" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2002"><a href="#cb78-2002" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2003"><a href="#cb78-2003" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2004"><a href="#cb78-2004" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2005"><a href="#cb78-2005" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2006"><a href="#cb78-2006" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2007"><a href="#cb78-2007" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2008"><a href="#cb78-2008" aria-hidden="true" tabindex="-1"></a>Volume_preparedness <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2009"><a href="#cb78-2009" aria-hidden="true" tabindex="-1"></a>Volume_preparedness<span class="sc">$</span>preparednessVolume <span class="ot">&lt;-</span> Volume_preparedness<span class="sc">$</span>Value</span>
<span id="cb78-2010"><a href="#cb78-2010" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2011"><a href="#cb78-2011" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2012"><a href="#cb78-2012" aria-hidden="true" tabindex="-1"></a><span class="do">###  relief</span></span>
<span id="cb78-2013"><a href="#cb78-2013" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;government relief&#39; SourceCountry:MI&quot;</span></span>
<span id="cb78-2014"><a href="#cb78-2014" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2015"><a href="#cb78-2015" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2016"><a href="#cb78-2016" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=TimelineVol&amp;startdatetime=&quot;</span></span>
<span id="cb78-2017"><a href="#cb78-2017" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2018"><a href="#cb78-2018" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2019"><a href="#cb78-2019" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2020"><a href="#cb78-2020" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2021"><a href="#cb78-2021" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2022"><a href="#cb78-2022" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2023"><a href="#cb78-2023" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2024"><a href="#cb78-2024" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2025"><a href="#cb78-2025" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2026"><a href="#cb78-2026" aria-hidden="true" tabindex="-1"></a>Volume_relief <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2027"><a href="#cb78-2027" aria-hidden="true" tabindex="-1"></a>Volume_relief<span class="sc">$</span>reliefVolume <span class="ot">&lt;-</span> Volume_relief<span class="sc">$</span>Value</span>
<span id="cb78-2028"><a href="#cb78-2028" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2029"><a href="#cb78-2029" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2030"><a href="#cb78-2030" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2031"><a href="#cb78-2031" aria-hidden="true" tabindex="-1"></a>Volume_test <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_relief, Volume_preparedness, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb78-2032"><a href="#cb78-2032" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2033"><a href="#cb78-2033" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2034"><a href="#cb78-2034" aria-hidden="true" tabindex="-1"></a>mi_overview<span class="ot">&lt;-</span>Volume_test <span class="sc">%&gt;%</span></span>
<span id="cb78-2035"><a href="#cb78-2035" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,preparednessVolume,reliefVolume)<span class="sc">%&gt;%</span></span>
<span id="cb78-2036"><a href="#cb78-2036" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-2037"><a href="#cb78-2037" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-2038"><a href="#cb78-2038" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable,<span class="at">alpha=</span><span class="fl">0.3</span>)) <span class="sc">+</span></span>
<span id="cb78-2039"><a href="#cb78-2039" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2040"><a href="#cb78-2040" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2041"><a href="#cb78-2041" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb78-2042"><a href="#cb78-2042" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb78-2043"><a href="#cb78-2043" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb78-2044"><a href="#cb78-2044" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb78-2045"><a href="#cb78-2045" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb78-2046"><a href="#cb78-2046" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-2047"><a href="#cb78-2047" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;government preparedness&quot;</span>, <span class="st">&quot;government relief&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-2048"><a href="#cb78-2048" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, Malawi&quot;</span>)</span>
<span id="cb78-2049"><a href="#cb78-2049" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2050"><a href="#cb78-2050" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2051"><a href="#cb78-2051" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2052"><a href="#cb78-2052" aria-hidden="true" tabindex="-1"></a>news_pre_rel1<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(ww_overview,mi_overview,</span>
<span id="cb78-2053"><a href="#cb78-2053" aria-hidden="true" tabindex="-1"></a>                      <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>,<span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-2054"><a href="#cb78-2054" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2055"><a href="#cb78-2055" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2056"><a href="#cb78-2056" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2057"><a href="#cb78-2057" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2058"><a href="#cb78-2058" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2059"><a href="#cb78-2059" aria-hidden="true" tabindex="-1"></a><span class="co"># 2. disaster </span></span>
<span id="cb78-2060"><a href="#cb78-2060" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2061"><a href="#cb78-2061" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2062"><a href="#cb78-2062" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2063"><a href="#cb78-2063" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2064"><a href="#cb78-2064" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2065"><a href="#cb78-2065" aria-hidden="true" tabindex="-1"></a><span class="do">### preparedness</span></span>
<span id="cb78-2066"><a href="#cb78-2066" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;disaster preparedness&#39;&quot;</span></span>
<span id="cb78-2067"><a href="#cb78-2067" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2068"><a href="#cb78-2068" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2069"><a href="#cb78-2069" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-2070"><a href="#cb78-2070" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2071"><a href="#cb78-2071" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2072"><a href="#cb78-2072" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2073"><a href="#cb78-2073" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2074"><a href="#cb78-2074" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2075"><a href="#cb78-2075" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2076"><a href="#cb78-2076" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2077"><a href="#cb78-2077" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2078"><a href="#cb78-2078" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2079"><a href="#cb78-2079" aria-hidden="true" tabindex="-1"></a>Volume_preparedness <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2080"><a href="#cb78-2080" aria-hidden="true" tabindex="-1"></a>Volume_preparedness<span class="sc">$</span>preparednessVolume <span class="ot">&lt;-</span> Volume_preparedness<span class="sc">$</span>Value</span>
<span id="cb78-2081"><a href="#cb78-2081" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2082"><a href="#cb78-2082" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2083"><a href="#cb78-2083" aria-hidden="true" tabindex="-1"></a><span class="do">###  relief</span></span>
<span id="cb78-2084"><a href="#cb78-2084" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;disaster relief&#39;&quot;</span></span>
<span id="cb78-2085"><a href="#cb78-2085" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2086"><a href="#cb78-2086" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2087"><a href="#cb78-2087" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-2088"><a href="#cb78-2088" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2089"><a href="#cb78-2089" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2090"><a href="#cb78-2090" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2091"><a href="#cb78-2091" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2092"><a href="#cb78-2092" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2093"><a href="#cb78-2093" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2094"><a href="#cb78-2094" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2095"><a href="#cb78-2095" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2096"><a href="#cb78-2096" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2097"><a href="#cb78-2097" aria-hidden="true" tabindex="-1"></a>Volume_relief <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2098"><a href="#cb78-2098" aria-hidden="true" tabindex="-1"></a>Volume_relief<span class="sc">$</span>reliefVolume <span class="ot">&lt;-</span> Volume_relief<span class="sc">$</span>Value</span>
<span id="cb78-2099"><a href="#cb78-2099" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2100"><a href="#cb78-2100" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2101"><a href="#cb78-2101" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2102"><a href="#cb78-2102" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb78-2103"><a href="#cb78-2103" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_preparedness, Volume_relief, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb78-2104"><a href="#cb78-2104" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2105"><a href="#cb78-2105" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2106"><a href="#cb78-2106" aria-hidden="true" tabindex="-1"></a>ww_overview2<span class="ot">&lt;-</span>Volume_total <span class="sc">%&gt;%</span></span>
<span id="cb78-2107"><a href="#cb78-2107" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,preparednessVolume,reliefVolume)<span class="sc">%&gt;%</span></span>
<span id="cb78-2108"><a href="#cb78-2108" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-2109"><a href="#cb78-2109" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-2110"><a href="#cb78-2110" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable)) <span class="sc">+</span></span>
<span id="cb78-2111"><a href="#cb78-2111" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2112"><a href="#cb78-2112" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2113"><a href="#cb78-2113" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb78-2114"><a href="#cb78-2114" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb78-2115"><a href="#cb78-2115" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb78-2116"><a href="#cb78-2116" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb78-2117"><a href="#cb78-2117" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb78-2118"><a href="#cb78-2118" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-2119"><a href="#cb78-2119" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;disaster preparedness&quot;</span>, <span class="st">&quot;disaster relief&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-2120"><a href="#cb78-2120" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, world-wide&quot;</span>)</span>
<span id="cb78-2121"><a href="#cb78-2121" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2122"><a href="#cb78-2122" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2123"><a href="#cb78-2123" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-2124"><a href="#cb78-2124" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-2125"><a href="#cb78-2125" aria-hidden="true" tabindex="-1"></a><span class="co"># Malawi MI</span></span>
<span id="cb78-2126"><a href="#cb78-2126" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-2127"><a href="#cb78-2127" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-2128"><a href="#cb78-2128" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2129"><a href="#cb78-2129" aria-hidden="true" tabindex="-1"></a><span class="do">###  preparedness</span></span>
<span id="cb78-2130"><a href="#cb78-2130" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;disaster preparedness&#39; SourceCountry:MI&quot;</span></span>
<span id="cb78-2131"><a href="#cb78-2131" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2132"><a href="#cb78-2132" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2133"><a href="#cb78-2133" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=TimelineVol&amp;startdatetime=&quot;</span></span>
<span id="cb78-2134"><a href="#cb78-2134" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2135"><a href="#cb78-2135" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2136"><a href="#cb78-2136" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2137"><a href="#cb78-2137" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2138"><a href="#cb78-2138" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2139"><a href="#cb78-2139" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2140"><a href="#cb78-2140" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2141"><a href="#cb78-2141" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2142"><a href="#cb78-2142" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2143"><a href="#cb78-2143" aria-hidden="true" tabindex="-1"></a>Volume_preparedness <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2144"><a href="#cb78-2144" aria-hidden="true" tabindex="-1"></a>Volume_preparedness<span class="sc">$</span>preparednessVolume <span class="ot">&lt;-</span> Volume_preparedness<span class="sc">$</span>Value</span>
<span id="cb78-2145"><a href="#cb78-2145" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2146"><a href="#cb78-2146" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2147"><a href="#cb78-2147" aria-hidden="true" tabindex="-1"></a><span class="do">###  relief</span></span>
<span id="cb78-2148"><a href="#cb78-2148" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;disaster relief&#39; SourceCountry:MI&quot;</span></span>
<span id="cb78-2149"><a href="#cb78-2149" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2150"><a href="#cb78-2150" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2151"><a href="#cb78-2151" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=TimelineVol&amp;startdatetime=&quot;</span></span>
<span id="cb78-2152"><a href="#cb78-2152" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2153"><a href="#cb78-2153" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2154"><a href="#cb78-2154" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2155"><a href="#cb78-2155" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2156"><a href="#cb78-2156" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2157"><a href="#cb78-2157" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2158"><a href="#cb78-2158" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2159"><a href="#cb78-2159" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2160"><a href="#cb78-2160" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2161"><a href="#cb78-2161" aria-hidden="true" tabindex="-1"></a>Volume_relief <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2162"><a href="#cb78-2162" aria-hidden="true" tabindex="-1"></a>Volume_relief<span class="sc">$</span>reliefVolume <span class="ot">&lt;-</span> Volume_relief<span class="sc">$</span>Value</span>
<span id="cb78-2163"><a href="#cb78-2163" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2164"><a href="#cb78-2164" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2165"><a href="#cb78-2165" aria-hidden="true" tabindex="-1"></a><span class="do">### natural disaster </span></span>
<span id="cb78-2166"><a href="#cb78-2166" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;natural disaster&#39; SourceCountry:MI&quot;</span></span>
<span id="cb78-2167"><a href="#cb78-2167" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2168"><a href="#cb78-2168" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2169"><a href="#cb78-2169" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-2170"><a href="#cb78-2170" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2171"><a href="#cb78-2171" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2172"><a href="#cb78-2172" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2173"><a href="#cb78-2173" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2174"><a href="#cb78-2174" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2175"><a href="#cb78-2175" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2176"><a href="#cb78-2176" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2177"><a href="#cb78-2177" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2178"><a href="#cb78-2178" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2179"><a href="#cb78-2179" aria-hidden="true" tabindex="-1"></a>Volume_disaster <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2180"><a href="#cb78-2180" aria-hidden="true" tabindex="-1"></a>Volume_disaster<span class="sc">$</span>disasterVolume <span class="ot">&lt;-</span> Volume_disaster<span class="sc">$</span>Value</span>
<span id="cb78-2181"><a href="#cb78-2181" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2182"><a href="#cb78-2182" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb78-2183"><a href="#cb78-2183" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_preparedness, Volume_relief, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb78-2184"><a href="#cb78-2184" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2185"><a href="#cb78-2185" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2186"><a href="#cb78-2186" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2187"><a href="#cb78-2187" aria-hidden="true" tabindex="-1"></a>Volume_test <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_total, Volume_disaster, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb78-2188"><a href="#cb78-2188" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2189"><a href="#cb78-2189" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2190"><a href="#cb78-2190" aria-hidden="true" tabindex="-1"></a>mi_overview2<span class="ot">&lt;-</span>Volume_test <span class="sc">%&gt;%</span></span>
<span id="cb78-2191"><a href="#cb78-2191" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,preparednessVolume,reliefVolume)<span class="sc">%&gt;%</span></span>
<span id="cb78-2192"><a href="#cb78-2192" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-2193"><a href="#cb78-2193" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-2194"><a href="#cb78-2194" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable,<span class="at">alpha=</span><span class="fl">0.3</span>)) <span class="sc">+</span></span>
<span id="cb78-2195"><a href="#cb78-2195" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2196"><a href="#cb78-2196" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2197"><a href="#cb78-2197" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb78-2198"><a href="#cb78-2198" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb78-2199"><a href="#cb78-2199" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb78-2200"><a href="#cb78-2200" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb78-2201"><a href="#cb78-2201" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb78-2202"><a href="#cb78-2202" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-2203"><a href="#cb78-2203" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;disaster preparedness&quot;</span>, <span class="st">&quot;disaster relief&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-2204"><a href="#cb78-2204" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, Malawi&quot;</span>)</span>
<span id="cb78-2205"><a href="#cb78-2205" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2206"><a href="#cb78-2206" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2207"><a href="#cb78-2207" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2208"><a href="#cb78-2208" aria-hidden="true" tabindex="-1"></a>news_pre_rel2<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(ww_overview2,mi_overview2,</span>
<span id="cb78-2209"><a href="#cb78-2209" aria-hidden="true" tabindex="-1"></a>                         <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>,<span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-2210"><a href="#cb78-2210" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2211"><a href="#cb78-2211" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2212"><a href="#cb78-2212" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2213"><a href="#cb78-2213" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2214"><a href="#cb78-2214" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2215"><a href="#cb78-2215" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2216"><a href="#cb78-2216" aria-hidden="true" tabindex="-1"></a><span class="co"># 3. Benchmark </span></span>
<span id="cb78-2217"><a href="#cb78-2217" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2218"><a href="#cb78-2218" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2219"><a href="#cb78-2219" aria-hidden="true" tabindex="-1"></a><span class="do">########################</span></span>
<span id="cb78-2220"><a href="#cb78-2220" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2221"><a href="#cb78-2221" aria-hidden="true" tabindex="-1"></a><span class="do">### taxation</span></span>
<span id="cb78-2222"><a href="#cb78-2222" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;taxation&#39;&quot;</span></span>
<span id="cb78-2223"><a href="#cb78-2223" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2224"><a href="#cb78-2224" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2225"><a href="#cb78-2225" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-2226"><a href="#cb78-2226" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2227"><a href="#cb78-2227" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2228"><a href="#cb78-2228" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2229"><a href="#cb78-2229" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2230"><a href="#cb78-2230" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2231"><a href="#cb78-2231" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2232"><a href="#cb78-2232" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2233"><a href="#cb78-2233" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2234"><a href="#cb78-2234" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2235"><a href="#cb78-2235" aria-hidden="true" tabindex="-1"></a>Volume_taxation <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2236"><a href="#cb78-2236" aria-hidden="true" tabindex="-1"></a>Volume_taxation<span class="sc">$</span>taxationVolume <span class="ot">&lt;-</span> Volume_taxation<span class="sc">$</span>Value</span>
<span id="cb78-2237"><a href="#cb78-2237" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2238"><a href="#cb78-2238" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2239"><a href="#cb78-2239" aria-hidden="true" tabindex="-1"></a><span class="do">###  unemployment</span></span>
<span id="cb78-2240"><a href="#cb78-2240" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;unemployment&#39;&quot;</span></span>
<span id="cb78-2241"><a href="#cb78-2241" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2242"><a href="#cb78-2242" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2243"><a href="#cb78-2243" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-2244"><a href="#cb78-2244" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2245"><a href="#cb78-2245" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2246"><a href="#cb78-2246" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2247"><a href="#cb78-2247" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2248"><a href="#cb78-2248" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2249"><a href="#cb78-2249" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2250"><a href="#cb78-2250" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2251"><a href="#cb78-2251" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2252"><a href="#cb78-2252" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2253"><a href="#cb78-2253" aria-hidden="true" tabindex="-1"></a>Volume_unemployment <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2254"><a href="#cb78-2254" aria-hidden="true" tabindex="-1"></a>Volume_unemployment<span class="sc">$</span>unemploymentVolume <span class="ot">&lt;-</span> Volume_unemployment<span class="sc">$</span>Value</span>
<span id="cb78-2255"><a href="#cb78-2255" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2256"><a href="#cb78-2256" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2257"><a href="#cb78-2257" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2258"><a href="#cb78-2258" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb78-2259"><a href="#cb78-2259" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_taxation, Volume_unemployment, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb78-2260"><a href="#cb78-2260" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2261"><a href="#cb78-2261" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2262"><a href="#cb78-2262" aria-hidden="true" tabindex="-1"></a>ww_overview<span class="ot">&lt;-</span>Volume_total <span class="sc">%&gt;%</span></span>
<span id="cb78-2263"><a href="#cb78-2263" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,taxationVolume,unemploymentVolume)<span class="sc">%&gt;%</span></span>
<span id="cb78-2264"><a href="#cb78-2264" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-2265"><a href="#cb78-2265" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-2266"><a href="#cb78-2266" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable)) <span class="sc">+</span></span>
<span id="cb78-2267"><a href="#cb78-2267" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2268"><a href="#cb78-2268" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2269"><a href="#cb78-2269" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb78-2270"><a href="#cb78-2270" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb78-2271"><a href="#cb78-2271" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb78-2272"><a href="#cb78-2272" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb78-2273"><a href="#cb78-2273" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb78-2274"><a href="#cb78-2274" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-2275"><a href="#cb78-2275" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;taxation&quot;</span>, <span class="st">&quot;unemployment&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-2276"><a href="#cb78-2276" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, world-wide&quot;</span>)</span>
<span id="cb78-2277"><a href="#cb78-2277" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2278"><a href="#cb78-2278" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2279"><a href="#cb78-2279" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-2280"><a href="#cb78-2280" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-2281"><a href="#cb78-2281" aria-hidden="true" tabindex="-1"></a><span class="co"># Malawi MI</span></span>
<span id="cb78-2282"><a href="#cb78-2282" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-2283"><a href="#cb78-2283" aria-hidden="true" tabindex="-1"></a><span class="do">############################</span></span>
<span id="cb78-2284"><a href="#cb78-2284" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2285"><a href="#cb78-2285" aria-hidden="true" tabindex="-1"></a><span class="do">### taxation</span></span>
<span id="cb78-2286"><a href="#cb78-2286" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;taxation&#39; SourceCountry:MI&quot;</span></span>
<span id="cb78-2287"><a href="#cb78-2287" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2288"><a href="#cb78-2288" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2289"><a href="#cb78-2289" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-2290"><a href="#cb78-2290" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2291"><a href="#cb78-2291" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2292"><a href="#cb78-2292" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2293"><a href="#cb78-2293" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2294"><a href="#cb78-2294" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2295"><a href="#cb78-2295" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2296"><a href="#cb78-2296" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2297"><a href="#cb78-2297" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2298"><a href="#cb78-2298" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2299"><a href="#cb78-2299" aria-hidden="true" tabindex="-1"></a>Volume_taxation <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2300"><a href="#cb78-2300" aria-hidden="true" tabindex="-1"></a>Volume_taxation<span class="sc">$</span>taxationVolume <span class="ot">&lt;-</span> Volume_taxation<span class="sc">$</span>Value</span>
<span id="cb78-2301"><a href="#cb78-2301" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2302"><a href="#cb78-2302" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2303"><a href="#cb78-2303" aria-hidden="true" tabindex="-1"></a><span class="do">###  unemployment</span></span>
<span id="cb78-2304"><a href="#cb78-2304" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="st">&quot;&#39;unemployment&#39; SourceCountry:MI&quot;</span></span>
<span id="cb78-2305"><a href="#cb78-2305" aria-hidden="true" tabindex="-1"></a><span class="co">#Building the Volume dataframe</span></span>
<span id="cb78-2306"><a href="#cb78-2306" aria-hidden="true" tabindex="-1"></a>vp1 <span class="ot">&lt;-</span> <span class="st">&quot;https://api.gdeltproject.org/api/v2/doc/doc?query=&quot;</span></span>
<span id="cb78-2307"><a href="#cb78-2307" aria-hidden="true" tabindex="-1"></a>vp2 <span class="ot">&lt;-</span> <span class="st">&quot;&amp;mode=timelinevolinfo&amp;startdatetime=&quot;</span></span>
<span id="cb78-2308"><a href="#cb78-2308" aria-hidden="true" tabindex="-1"></a>vp3 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;enddatetime=&quot;</span></span>
<span id="cb78-2309"><a href="#cb78-2309" aria-hidden="true" tabindex="-1"></a>vp4 <span class="ot">&lt;-</span> <span class="st">&quot;000000&amp;format=CSV&quot;</span></span>
<span id="cb78-2310"><a href="#cb78-2310" aria-hidden="true" tabindex="-1"></a>text_v_url <span class="ot">&lt;-</span> <span class="fu">paste0</span>(vp1, query, vp2, startdate, vp3, enddate, vp4)</span>
<span id="cb78-2311"><a href="#cb78-2311" aria-hidden="true" tabindex="-1"></a>v_url <span class="ot">&lt;-</span> <span class="fu">URLencode</span>(text_v_url)</span>
<span id="cb78-2312"><a href="#cb78-2312" aria-hidden="true" tabindex="-1"></a>v_url</span>
<span id="cb78-2313"><a href="#cb78-2313" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2314"><a href="#cb78-2314" aria-hidden="true" tabindex="-1"></a>Volume <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(v_url)</span>
<span id="cb78-2315"><a href="#cb78-2315" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2316"><a href="#cb78-2316" aria-hidden="true" tabindex="-1"></a>Volume<span class="sc">$</span>Date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(Volume<span class="sc">$</span>Date, <span class="st">&quot;%Y-%m-%d&quot;</span>)</span>
<span id="cb78-2317"><a href="#cb78-2317" aria-hidden="true" tabindex="-1"></a>Volume_unemployment <span class="ot">&lt;-</span> Volume</span>
<span id="cb78-2318"><a href="#cb78-2318" aria-hidden="true" tabindex="-1"></a>Volume_unemployment<span class="sc">$</span>unemploymentVolume <span class="ot">&lt;-</span> Volume_unemployment<span class="sc">$</span>Value</span>
<span id="cb78-2319"><a href="#cb78-2319" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2320"><a href="#cb78-2320" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2321"><a href="#cb78-2321" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2322"><a href="#cb78-2322" aria-hidden="true" tabindex="-1"></a><span class="do">### Merging</span></span>
<span id="cb78-2323"><a href="#cb78-2323" aria-hidden="true" tabindex="-1"></a>Volume_total <span class="ot">&lt;-</span> <span class="fu">merge</span>(Volume_taxation, Volume_unemployment, <span class="at">by =</span> <span class="st">&quot;Date&quot;</span>) </span>
<span id="cb78-2324"><a href="#cb78-2324" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2325"><a href="#cb78-2325" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2326"><a href="#cb78-2326" aria-hidden="true" tabindex="-1"></a>mi_overview<span class="ot">&lt;-</span>Volume_total <span class="sc">%&gt;%</span></span>
<span id="cb78-2327"><a href="#cb78-2327" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Date,taxationVolume,unemploymentVolume)<span class="sc">%&gt;%</span></span>
<span id="cb78-2328"><a href="#cb78-2328" aria-hidden="true" tabindex="-1"></a>  reshape2<span class="sc">::</span><span class="fu">melt</span>( <span class="at">id.var=</span><span class="st">&#39;Date&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-2329"><a href="#cb78-2329" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb78-2330"><a href="#cb78-2330" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>( <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> value, <span class="at">col=</span>variable,<span class="at">alpha=</span><span class="fl">0.3</span>)) <span class="sc">+</span></span>
<span id="cb78-2331"><a href="#cb78-2331" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>( <span class="at">date_labels =</span> <span class="st">&quot;%Y&quot;</span>,<span class="at">date_breaks =</span> <span class="st">&#39;year&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2332"><a href="#cb78-2332" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&#39;Date&#39;</span>) <span class="sc">+</span></span>
<span id="cb78-2333"><a href="#cb78-2333" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;coverage as % of total article count&quot;</span>)<span class="sc">+</span> </span>
<span id="cb78-2334"><a href="#cb78-2334" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()<span class="sc">+</span></span>
<span id="cb78-2335"><a href="#cb78-2335" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>),</span>
<span id="cb78-2336"><a href="#cb78-2336" aria-hidden="true" tabindex="-1"></a>        <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>,<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb78-2337"><a href="#cb78-2337" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">14</span>), <span class="co">#change legend title font size</span></span>
<span id="cb78-2338"><a href="#cb78-2338" aria-hidden="true" tabindex="-1"></a>        <span class="at">legend.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))<span class="sc">+</span></span>
<span id="cb78-2339"><a href="#cb78-2339" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;taxation&quot;</span>, <span class="st">&quot;unemployment&quot;</span>), <span class="at">name =</span> <span class="st">&quot;Article phrase&quot;</span>)<span class="sc">+</span></span>
<span id="cb78-2340"><a href="#cb78-2340" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;news coverage, Malawi&quot;</span>)</span>
<span id="cb78-2341"><a href="#cb78-2341" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2342"><a href="#cb78-2342" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2343"><a href="#cb78-2343" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2344"><a href="#cb78-2344" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2345"><a href="#cb78-2345" aria-hidden="true" tabindex="-1"></a>news_benchmark<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(ww_overview,mi_overview,</span>
<span id="cb78-2346"><a href="#cb78-2346" aria-hidden="true" tabindex="-1"></a>                      <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>,<span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-2347"><a href="#cb78-2347" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2348"><a href="#cb78-2348" aria-hidden="true" tabindex="-1"></a>plot_media<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(news_pre_rel1,news_pre_rel2,news_benchmark,</span>
<span id="cb78-2349"><a href="#cb78-2349" aria-hidden="true" tabindex="-1"></a>                          <span class="at">ncol =</span> <span class="dv">1</span>, <span class="at">nrow =</span> <span class="dv">3</span>)</span>
<span id="cb78-2350"><a href="#cb78-2350" aria-hidden="true" tabindex="-1"></a>plot_media</span>
<span id="cb78-2351"><a href="#cb78-2351" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2352"><a href="#cb78-2352" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A18_media.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">12</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-2353"><a href="#cb78-2353" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2354"><a href="#cb78-2354" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2355"><a href="#cb78-2355" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-2356"><a href="#cb78-2356" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2357"><a href="#cb78-2357" aria-hidden="true" tabindex="-1"></a><span class="fu">## Figure A19: Malawi: Usage Frequency of Various Media</span></span>
<span id="cb78-2358"><a href="#cb78-2358" aria-hidden="true" tabindex="-1"></a><span class="in">```{r , echo=TRUE,warning=FALSE}</span></span>
<span id="cb78-2359"><a href="#cb78-2359" aria-hidden="true" tabindex="-1"></a><span class="co"># load afro-barometer 8 covariates</span></span>
<span id="cb78-2360"><a href="#cb78-2360" aria-hidden="true" tabindex="-1"></a>afro_8 <span class="ot">&lt;-</span> <span class="fu">read.spss</span>(<span class="st">&quot;1_input/afrobarometer_release-dataset_merge-34ctry_r8_en_2023-03-01.sav&quot;</span>, <span class="at">to.data.frame=</span><span class="cn">TRUE</span>)  <span class="sc">%&gt;%</span></span>
<span id="cb78-2361"><a href="#cb78-2361" aria-hidden="true" tabindex="-1"></a>   dplyr<span class="sc">::</span><span class="fu">filter</span>(COUNTRY<span class="sc">==</span><span class="st">&quot;Malawi&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-2362"><a href="#cb78-2362" aria-hidden="true" tabindex="-1"></a>  <span class="co"># select variables about news consumption</span></span>
<span id="cb78-2363"><a href="#cb78-2363" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(COUNTRY,RESPNO,Q55A,Q55B,Q55C,Q55D,Q55E)  <span class="sc">%&gt;%</span></span>
<span id="cb78-2364"><a href="#cb78-2364" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">radio =</span> Q55A,</span>
<span id="cb78-2365"><a href="#cb78-2365" aria-hidden="true" tabindex="-1"></a>                <span class="at">television =</span> Q55B,</span>
<span id="cb78-2366"><a href="#cb78-2366" aria-hidden="true" tabindex="-1"></a>                <span class="at">newspapers =</span> Q55C,</span>
<span id="cb78-2367"><a href="#cb78-2367" aria-hidden="true" tabindex="-1"></a>                <span class="at">internet =</span> Q55D,</span>
<span id="cb78-2368"><a href="#cb78-2368" aria-hidden="true" tabindex="-1"></a>                <span class="at">social_media  =</span> Q55E) </span>
<span id="cb78-2369"><a href="#cb78-2369" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2370"><a href="#cb78-2370" aria-hidden="true" tabindex="-1"></a><span class="co"># Now let us talk about the media and how you get information about politics and other issues. 55. How often do you get news from the following sources? </span></span>
<span id="cb78-2371"><a href="#cb78-2371" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2372"><a href="#cb78-2372" aria-hidden="true" tabindex="-1"></a>long_afro_8 <span class="ot">&lt;-</span> afro_8 <span class="sc">%&gt;%</span></span>
<span id="cb78-2373"><a href="#cb78-2373" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="at">cols =</span> radio<span class="sc">:</span>social_media, <span class="at">names_to =</span> <span class="st">&quot;Media&quot;</span>, <span class="at">values_to =</span> <span class="st">&quot;Frequency&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-2374"><a href="#cb78-2374" aria-hidden="true" tabindex="-1"></a>  <span class="fu">drop_na</span>(Frequency) <span class="co"># Optionally drop NA values if any</span></span>
<span id="cb78-2375"><a href="#cb78-2375" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2376"><a href="#cb78-2376" aria-hidden="true" tabindex="-1"></a><span class="co"># Calculate percentages</span></span>
<span id="cb78-2377"><a href="#cb78-2377" aria-hidden="true" tabindex="-1"></a>percent_data <span class="ot">&lt;-</span> long_afro_8 <span class="sc">%&gt;%</span></span>
<span id="cb78-2378"><a href="#cb78-2378" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">group_by</span>(Media, Frequency) <span class="sc">%&gt;%</span></span>
<span id="cb78-2379"><a href="#cb78-2379" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">summarise</span>(<span class="at">Count =</span> dplyr<span class="sc">::</span><span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">&#39;drop&#39;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb78-2380"><a href="#cb78-2380" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">group_by</span>(Media) <span class="sc">%&gt;%</span></span>
<span id="cb78-2381"><a href="#cb78-2381" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">Percentage =</span> Count <span class="sc">/</span> <span class="fu">sum</span>(Count) <span class="sc">*</span> <span class="dv">100</span>)</span>
<span id="cb78-2382"><a href="#cb78-2382" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2383"><a href="#cb78-2383" aria-hidden="true" tabindex="-1"></a>fig_malawi_media<span class="ot">&lt;-</span><span class="fu">ggplot</span>(percent_data, <span class="fu">aes</span>(<span class="at">x =</span> Frequency, <span class="at">y =</span> Percentage, <span class="at">fill =</span> Frequency)) <span class="sc">+</span></span>
<span id="cb78-2384"><a href="#cb78-2384" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-2385"><a href="#cb78-2385" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> Media, <span class="at">scales =</span> <span class="st">&quot;free_x&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-2386"><a href="#cb78-2386" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">18</span>) <span class="sc">+</span></span>
<span id="cb78-2387"><a href="#cb78-2387" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;&quot;</span>, <span class="at">y =</span> <span class="st">&quot;Percentage&quot;</span>, <span class="at">title =</span> <span class="st">&quot;Malawi: Usage Frequency of Various Media&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-2388"><a href="#cb78-2388" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb78-2389"><a href="#cb78-2389" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;none&quot;</span>)</span>
<span id="cb78-2390"><a href="#cb78-2390" aria-hidden="true" tabindex="-1"></a>fig_malawi_media</span>
<span id="cb78-2391"><a href="#cb78-2391" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2392"><a href="#cb78-2392" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A19_malawi_media.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">14</span>, <span class="at">height =</span> <span class="dv">9</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-2393"><a href="#cb78-2393" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2394"><a href="#cb78-2394" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb78-2395"><a href="#cb78-2395" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2396"><a href="#cb78-2396" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2397"><a href="#cb78-2397" aria-hidden="true" tabindex="-1"></a>Figure A20: EU Speeches: Relative importance of topics between 2007 to 2015</span>
<span id="cb78-2398"><a href="#cb78-2398" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2399"><a href="#cb78-2399" aria-hidden="true" tabindex="-1"></a><span class="in">```{r EU,warning=FALSE}</span></span>
<span id="cb78-2400"><a href="#cb78-2400" aria-hidden="true" tabindex="-1"></a><span class="do">#######################################</span></span>
<span id="cb78-2401"><a href="#cb78-2401" aria-hidden="true" tabindex="-1"></a><span class="co"># EU speeches</span></span>
<span id="cb78-2402"><a href="#cb78-2402" aria-hidden="true" tabindex="-1"></a><span class="do">#######################################</span></span>
<span id="cb78-2403"><a href="#cb78-2403" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2404"><a href="#cb78-2404" aria-hidden="true" tabindex="-1"></a><span class="fu">load</span>(<span class="st">&quot;1_input/euspeech.korpus.RData&quot;</span>)</span>
<span id="cb78-2405"><a href="#cb78-2405" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2406"><a href="#cb78-2406" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2407"><a href="#cb78-2407" aria-hidden="true" tabindex="-1"></a><span class="co"># Applying the Policy-Agendas und Laver-Garry dictionaries</span></span>
<span id="cb78-2408"><a href="#cb78-2408" aria-hidden="true" tabindex="-1"></a><span class="fu">load</span>(<span class="st">&quot;1_input/policy_agendas_english.RData&quot;</span>)</span>
<span id="cb78-2409"><a href="#cb78-2409" aria-hidden="true" tabindex="-1"></a>policyagendas.lexicon <span class="ot">&lt;-</span> <span class="fu">dictionary</span>(dictLexic2Topics)</span>
<span id="cb78-2410"><a href="#cb78-2410" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2411"><a href="#cb78-2411" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2412"><a href="#cb78-2412" aria-hidden="true" tabindex="-1"></a>dfm.eu.pa <span class="ot">&lt;-</span> <span class="fu">dfm</span>(korpus.euspeech, <span class="at">dfm_group =</span> <span class="st">&quot;country&quot;</span>, <span class="at">dictionary =</span> policyagendas.lexicon)</span>
<span id="cb78-2413"><a href="#cb78-2413" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2414"><a href="#cb78-2414" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2415"><a href="#cb78-2415" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2416"><a href="#cb78-2416" aria-hidden="true" tabindex="-1"></a><span class="co"># Adding new categories with example keywords</span></span>
<span id="cb78-2417"><a href="#cb78-2417" aria-hidden="true" tabindex="-1"></a>dictLexic2Topics<span class="sc">$</span>disaster_preparedness <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;preparedness&quot;</span>,<span class="st">&quot;prevention&quot;</span>,<span class="st">&quot;disaster preparedness&quot;</span>,<span class="st">&quot;disaster prevention&quot;</span>,<span class="st">&quot;emergency preparedness&quot;</span>, <span class="st">&quot;emergency plan&quot;</span>, <span class="st">&quot;disaster plan&quot;</span>, <span class="st">&quot;evacuation plan&quot;</span>, <span class="st">&quot;crisis management&quot;</span>, <span class="st">&quot;early warning system&quot;</span>, <span class="st">&quot;emergency kit&quot;</span>, <span class="st">&quot;emergency supplies&quot;</span>, <span class="st">&quot;disaster risk reduction&quot;</span>, <span class="st">&quot;resilience building&quot;</span>, <span class="st">&quot;community preparedness&quot;</span>, <span class="st">&quot;disaster preparedness training&quot;</span>, <span class="st">&quot;hazard mitigation&quot;</span>, <span class="st">&quot;emergency response plan&quot;</span>, <span class="st">&quot;preparedness exercises&quot;</span>, <span class="st">&quot;business continuity planning&quot;</span>, <span class="st">&quot;disaster resilience&quot;</span>, <span class="st">&quot;emergency shelter&quot;</span>, <span class="st">&quot;food storage&quot;</span>, <span class="st">&quot;water purification&quot;</span>, <span class="st">&quot;first aid training&quot;</span>, <span class="st">&quot;public health preparedness&quot;</span>, <span class="st">&quot;disaster simulation&quot;</span>, <span class="st">&quot;evacuation route&quot;</span>, <span class="st">&quot;emergency communication system&quot;</span>)</span>
<span id="cb78-2418"><a href="#cb78-2418" aria-hidden="true" tabindex="-1"></a>dictLexic2Topics<span class="sc">$</span>disaster_relief <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;relief&quot;</span>,<span class="st">&quot;relief efforts&quot;</span>, <span class="st">&quot;aid distribution&quot;</span>, <span class="st">&quot;disaster recovery&quot;</span>, <span class="st">&quot;humanitarian aid&quot;</span>, <span class="st">&quot;emergency aid&quot;</span>, <span class="st">&quot;recovery operations&quot;</span>, <span class="st">&quot;disaster assistance&quot;</span>, <span class="st">&quot;rehabilitation efforts&quot;</span>, <span class="st">&quot;relief supplies&quot;</span>, <span class="st">&quot;medical relief&quot;</span>, <span class="st">&quot;search and rescue&quot;</span>, <span class="st">&quot;emergency medical services&quot;</span>, <span class="st">&quot;food aid&quot;</span>, <span class="st">&quot;shelter provision&quot;</span>, <span class="st">&quot;post-disaster recovery&quot;</span>, <span class="st">&quot;psychosocial support&quot;</span>, <span class="st">&quot;reconstruction assistance&quot;</span>, <span class="st">&quot;debris removal&quot;</span>, <span class="st">&quot;donation management&quot;</span>, <span class="st">&quot;volunteer coordination&quot;</span>, <span class="st">&quot;crisis counseling&quot;</span>, <span class="st">&quot;financial assistance&quot;</span>, <span class="st">&quot;temporary housing&quot;</span>, <span class="st">&quot;disaster victim identification&quot;</span>, <span class="st">&quot;infrastructure repair&quot;</span>)</span>
<span id="cb78-2419"><a href="#cb78-2419" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2420"><a href="#cb78-2420" aria-hidden="true" tabindex="-1"></a>policyagendas.lexicon <span class="ot">&lt;-</span> <span class="fu">dictionary</span>(dictLexic2Topics)</span>
<span id="cb78-2421"><a href="#cb78-2421" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2422"><a href="#cb78-2422" aria-hidden="true" tabindex="-1"></a><span class="co"># Ensure your corpus has the necessary metadata, including the date</span></span>
<span id="cb78-2423"><a href="#cb78-2423" aria-hidden="true" tabindex="-1"></a><span class="co"># This step is skipped here as it&#39;s assumed your corpus is already prepared</span></span>
<span id="cb78-2424"><a href="#cb78-2424" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2425"><a href="#cb78-2425" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a Document-Feature Matrix (DFM) using the dictionary for topic categorization</span></span>
<span id="cb78-2426"><a href="#cb78-2426" aria-hidden="true" tabindex="-1"></a>dfm.eu.pa <span class="ot">&lt;-</span> <span class="fu">dfm</span>(korpus.euspeech) <span class="sc">%&gt;%</span></span>
<span id="cb78-2427"><a href="#cb78-2427" aria-hidden="true" tabindex="-1"></a>  <span class="fu">dfm_lookup</span>(<span class="at">dictionary =</span> policyagendas.lexicon)</span>
<span id="cb78-2428"><a href="#cb78-2428" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2429"><a href="#cb78-2429" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2430"><a href="#cb78-2430" aria-hidden="true" tabindex="-1"></a><span class="co"># Assuming &#39;Jahr&#39; is the column in &#39;korpus.euspeech.stats&#39; with the year information</span></span>
<span id="cb78-2431"><a href="#cb78-2431" aria-hidden="true" tabindex="-1"></a><span class="co"># and it correctly aligns with the documents in your DFM</span></span>
<span id="cb78-2432"><a href="#cb78-2432" aria-hidden="true" tabindex="-1"></a><span class="fu">docvars</span>(dfm.eu.pa, <span class="st">&quot;year&quot;</span>) <span class="ot">&lt;-</span> korpus.euspeech.stats<span class="sc">$</span>Jahr</span>
<span id="cb78-2433"><a href="#cb78-2433" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2434"><a href="#cb78-2434" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2435"><a href="#cb78-2435" aria-hidden="true" tabindex="-1"></a><span class="co"># Check for any NA values in year assignment</span></span>
<span id="cb78-2436"><a href="#cb78-2436" aria-hidden="true" tabindex="-1"></a><span class="fu">sum</span>(<span class="fu">is.na</span>(<span class="fu">docvars</span>(dfm.eu.pa, <span class="st">&quot;year&quot;</span>)))</span>
<span id="cb78-2437"><a href="#cb78-2437" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2438"><a href="#cb78-2438" aria-hidden="true" tabindex="-1"></a>dfm_by_year <span class="ot">&lt;-</span> <span class="fu">dfm_group</span>(dfm.eu.pa, <span class="at">groups =</span> <span class="fu">docvars</span>(dfm.eu.pa, <span class="st">&quot;year&quot;</span>))</span>
<span id="cb78-2439"><a href="#cb78-2439" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2440"><a href="#cb78-2440" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert counts to proportions within each year</span></span>
<span id="cb78-2441"><a href="#cb78-2441" aria-hidden="true" tabindex="-1"></a>dfm_prop <span class="ot">&lt;-</span> <span class="fu">dfm_weight</span>(dfm_by_year, <span class="at">scheme =</span> <span class="st">&quot;prop&quot;</span>)</span>
<span id="cb78-2442"><a href="#cb78-2442" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2443"><a href="#cb78-2443" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2444"><a href="#cb78-2444" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert DFM to a dense matrix, then to a data frame</span></span>
<span id="cb78-2445"><a href="#cb78-2445" aria-hidden="true" tabindex="-1"></a>dfm_matrix <span class="ot">&lt;-</span> <span class="fu">as.matrix</span>(dfm_prop)</span>
<span id="cb78-2446"><a href="#cb78-2446" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">year =</span> <span class="fu">rownames</span>(dfm_matrix), dfm_matrix)</span>
<span id="cb78-2447"><a href="#cb78-2447" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2448"><a href="#cb78-2448" aria-hidden="true" tabindex="-1"></a><span class="co"># Use pivot_longer to convert wide data to long format</span></span>
<span id="cb78-2449"><a href="#cb78-2449" aria-hidden="true" tabindex="-1"></a>df_long <span class="ot">&lt;-</span> <span class="fu">pivot_longer</span>(df, <span class="at">cols =</span> <span class="sc">-</span>year, <span class="at">names_to =</span> <span class="st">&quot;topic&quot;</span>, <span class="at">values_to =</span> <span class="st">&quot;frequency&quot;</span>)</span>
<span id="cb78-2450"><a href="#cb78-2450" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2451"><a href="#cb78-2451" aria-hidden="true" tabindex="-1"></a><span class="co"># Note: If the year column is not character, convert it to avoid issues in ggplot</span></span>
<span id="cb78-2452"><a href="#cb78-2452" aria-hidden="true" tabindex="-1"></a>df_long<span class="sc">$</span>year <span class="ot">&lt;-</span> <span class="fu">as.character</span>(df_long<span class="sc">$</span>year)</span>
<span id="cb78-2453"><a href="#cb78-2453" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2454"><a href="#cb78-2454" aria-hidden="true" tabindex="-1"></a>target <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;disaster_relief&quot;</span>, <span class="st">&quot;disaster_preparedness&quot;</span>)</span>
<span id="cb78-2455"><a href="#cb78-2455" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2456"><a href="#cb78-2456" aria-hidden="true" tabindex="-1"></a>prep_relief<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span></span>
<span id="cb78-2457"><a href="#cb78-2457" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(frequency<span class="sc">&lt;</span><span class="fl">0.0025</span>)<span class="sc">%&gt;%</span></span>
<span id="cb78-2458"><a href="#cb78-2458" aria-hidden="true" tabindex="-1"></a>  <span class="co"># filter( topic %in% target)  %&gt;% # equivalently, dat %&gt;% filter(name %in% target)</span></span>
<span id="cb78-2459"><a href="#cb78-2459" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> year, <span class="at">y =</span> frequency, <span class="at">color =</span> topic, <span class="at">group =</span> topic)) <span class="sc">+</span></span>
<span id="cb78-2460"><a href="#cb78-2460" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb78-2461"><a href="#cb78-2461" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb78-2462"><a href="#cb78-2462" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Prepardness and Relief + similar importance&quot;</span>,</span>
<span id="cb78-2463"><a href="#cb78-2463" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Year&quot;</span>,</span>
<span id="cb78-2464"><a href="#cb78-2464" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Relative Importance&quot;</span>,</span>
<span id="cb78-2465"><a href="#cb78-2465" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Topic&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-2466"><a href="#cb78-2466" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>))<span class="sc">+</span></span>
<span id="cb78-2467"><a href="#cb78-2467" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-2468"><a href="#cb78-2468" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2469"><a href="#cb78-2469" aria-hidden="true" tabindex="-1"></a>all<span class="ot">&lt;-</span>df_long <span class="sc">%&gt;%</span></span>
<span id="cb78-2470"><a href="#cb78-2470" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(frequency<span class="sc">&gt;</span><span class="fl">0.05</span>)<span class="sc">%&gt;%</span></span>
<span id="cb78-2471"><a href="#cb78-2471" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>( <span class="fu">aes</span>(<span class="at">x =</span> year, <span class="at">y =</span> frequency, <span class="at">color =</span> topic, <span class="at">group =</span> topic)) <span class="sc">+</span></span>
<span id="cb78-2472"><a href="#cb78-2472" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb78-2473"><a href="#cb78-2473" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb78-2474"><a href="#cb78-2474" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Top 5 topics&quot;</span>,</span>
<span id="cb78-2475"><a href="#cb78-2475" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Year&quot;</span>,</span>
<span id="cb78-2476"><a href="#cb78-2476" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Relative Importance&quot;</span>,</span>
<span id="cb78-2477"><a href="#cb78-2477" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Topic&quot;</span>) <span class="sc">+</span></span>
<span id="cb78-2478"><a href="#cb78-2478" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>))<span class="sc">+</span></span>
<span id="cb78-2479"><a href="#cb78-2479" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>)</span>
<span id="cb78-2480"><a href="#cb78-2480" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2481"><a href="#cb78-2481" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2482"><a href="#cb78-2482" aria-hidden="true" tabindex="-1"></a>fig_eu_speech<span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(prep_relief,all,</span>
<span id="cb78-2483"><a href="#cb78-2483" aria-hidden="true" tabindex="-1"></a>                          <span class="at">ncol =</span> <span class="dv">2</span>, <span class="at">nrow =</span> <span class="dv">1</span>)</span>
<span id="cb78-2484"><a href="#cb78-2484" aria-hidden="true" tabindex="-1"></a>fig_eu_speech</span>
<span id="cb78-2485"><a href="#cb78-2485" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2486"><a href="#cb78-2486" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;2_output/fig_A20_eu_speech.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">12</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span>
<span id="cb78-2487"><a href="#cb78-2487" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb78-2488"><a href="#cb78-2488" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div></div></div></div></div>
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